Body-worn system for continuous, noninvasive measurement of cardiac output, stroke volume, cardiac power, and blood pressure

ABSTRACT

The invention provides a system for measuring stroke volume (SV), cardiac output (CO), and cardiac power (CP) from a patient that features: 1) an impedance sensor connected to at least two body-worn electrodes and including an impedance circuit that processes analog signals from the electrodes to measure an impedance signal (e.g. a TBEV waveform); 2) an ECG sensor connected to at least two chest-worn electrodes and including an ECG circuit that processes analog signals from the electrodes to measure and ECG signal; 3) an optical sensor connected to a body-worn optical probe and including an optical circuit that processes signals from the probe to measure at least one optical signal (e.g. a PPG waveform) from the patient; 4) a processing system, typically worn on the patient&#39;s wrist and connected through a wired interface to the optical sensor, and through either a wired or wireless interface to the TBEV and ECG sensors. The processing system analyzes the ECG, TBEV and optical signals to determine SV, and further analyzes SV and HR determined from an ECG sensor to determine CO.

CROSS REFERENCES TO RELATED APPLICATIONS

This application claims priority from a Provisional Application entitled‘BODY-WORN SYSTEM FOR CONTINUOUS, NONINVASIVE MEASUREMENT OF CARDIACOUTPUT, STROKE VOLUME, AND BLOOD PRESSURE’, U.S. Ser. No. 61/427,756,filed Dec. 28, 2010, which is hereby incorporated by reference in itsentirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Work for some of the components described in this patent application wassponsored by the Department of Defense under contract W81XWH-11-2-0085.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to medical devices for monitoringcardiovascular properties, e.g. cardiac output (CO), stroke volume (SV),and continuous non-invasive blood pressure (cNIBP).

2. Description of the Related Art

CO is typically measured in a hospital setting and, informally,indicates how efficiently a patient's heart pumps blood through theirarterial tree. More specifically, CO, with units of liters/minute,describes the time-dependent volume of blood ejected from the leftventricle into the aorta; it indicates how well the patient's heartdelivers blood-borne oxygen, nutrients, and other substances to thecells in the body. CO is the product of heart rate (HR) and SV, where SVis defined as the mathematical difference between left ventricular enddiastolic volume (EDV) and end systolic volume (ESV), i.e.:CO=SV×HR  (1)

Combining CO and mean arterial blood pressure (MAP) into a single value,called ‘cardiac power’ (CP), provides a particularly valuable prognosticvariable for monitoring patients suffering from cardiac conditions suchas congestive heart failure (CHF), and is an independent predictor ofmortality that can be measured non-invasively using cardiopulmonaryexercise testing. Specifically, CP is defined as:CP=CO×MAP  (2)

Measuring CO and SV in a continuous, non-invasive manner with highclinical accuracy has often been considered a ‘holy grail’ ofmedical-device monitoring. Most existing techniques in this fieldrequire in-dwelling catheters, which in turn can harm the patient, areinherently inaccurate in the critically, and require a specially trainedoperator. For example, current ‘gold standards’ for this measurement arethermodilution cardiac output (TDCO) and the Fick Oxygen Principal(Fick). However both TDCO and Fick are highly invasive techniques thatcan cause infection and other complications, even in carefullycontrolled hospital environments. In TDCO, a pulmonary artery catheter(PAC), also known as a Swan-Ganz catheter, is typically inserted intothe right portion of the patient's heart. Procedurally a bolus(typically 10 ml) of glucose or saline that is cooled to a knowntemperature is injected through the PAC. A temperature-measuring devicewithin the PAC, located a known distance away (typically 6-10 cm) fromwhere fluid is injected, measures the progressively increasingtemperature of the diluted blood. CO is then estimated from a measuredtime-temperature curve, called the ‘thermodilution curve’. The largerthe area under this curve, the lower the cardiac output. Likewise, asmaller the area under the curve implies a shorter transit time for thecold bolus to dissipate, hence a higher CO.

Fick involves calculating oxygen consumed and disseminated throughoutthe patient's blood over a given time period. An algorithm associatedwith the technique incorporates consumption of oxygen as measured with aspirometer with the difference in oxygen content of centralized bloodmeasured from a PAC and oxygen content of peripheral arterial bloodmeasured from an in-dwelling cannula.

Both TD and Fick typically measure CO with accuracies between about0.5-1.0 l/min, or about +/−20% in the critically ill.

Several non-invasive techniques for measuring SV/CO/CP have beendeveloped with the hope of curing the deficiencies of Fick and TD. Forexample, Doppler-based ultrasonic echo (Doppler/ultrasound) measuresblood velocity using the well-known Doppler shift, and has shownreasonable accuracy compared to more invasive methods. But both two andthree-dimensional versions of this technique require a specially trainedhuman operator, and are thus, with the exception of the esophagealDoppler technique, impractical for continuous measurements. CO/SV canalso be measured with techniques that rely on electrodes placed on thepatient's torso that inject and then collect a low-amperage,high-frequency modulated electrical current. These techniques, based onelectrical bioimpedance and called ‘impedance cardiography’ (ICG),‘electrical cardiometry velocimetry’ (ECV), and ‘bioreactance’ (BR),measure a time-dependent electrical waveform that is modulated by theflow of blood through the patient's thorax. Blood is a good electricalconductor, and when pumped by the heart can further modulate the currentinjected by these techniques in a manner sensitive to the patient's CO.During a measurement, ICG, ECV, and BR each extract properties calledleft ventricular ejection time (LVET) and pre-injection period (PEP)from time-dependent ICG and ECG waveforms. A processor then analyzes thewaveform with an empirical mathematical equation, shown below in Eq. 2,to estimate SV. CO is then determined from the product of SV and HR, asdescribed above in Eq. 1.

ICG, ECV, and BR all represent a continuous, non-invasive alternativefor measuring CO/SV, and in theory can be conducted with an inexpensivesystem and no specially trained operator. But the medical community hasnot embraced such methods, despite the fact that clinical studies haveshown them to be effective with some patient populations. In 1992, forexample, an analysis by Fuller et al. analyzed data from 75 publishedstudies describing the correlation between ICG and TD/Fick (Fuller etal., The validity of cardiac output measurement by thoracic impedance: ameta-analysis; Clinical Investigative Medicine; 15: 103-112 (1992)). Thestudy concluded using a meta analysis wherein, in 28 of these studies,ICG displayed a correlation of between r=0.80-0.83 against TDCO, dyedilution and Fick CO. Patients classified as critically ill, e.g. thosesuffering from acute myocardial infarction, sepsis, and excessive lungfluids, yielded worse results. Further impeding commercial acceptance ofthese techniques is the tendency of ICG monitors to be relatively bulkyand similar in both size and complexity to conventional vital signsmonitors. This means two large and expensive pieces of monitoringequipment may need to be located bedside in order to monitor a patient'svital signs and CO/SV. For this and other reasons, impedance-basedmeasurements of CO have not achieved widespread commercial success.

ICG-based methodologies for measuring CO/SV have evolved since Fuller'sanalysis. For example, it has recently been shown that the dimensionlesspeak rate of change of the trans-thoracic electrical impedance pulsevariation, which is defined as the maximum value of the derivative ofthe ICG waveform (dZ/dt)_(max) divided by the base impedance (Z_(o)), isan acceleration analog (with units of 1/s²). When subjected to squareroot transformation this yields ohmic mean velocity[(dZ/dt)_(max)/Zo)]^(0.5). These parameters are described in detail inU.S. Pat. Nos. 7,740,590 and 7,261,697, the contents of which are fullyincorporated herein by reference. Reasonable facsimiles of SV can beobtained when this value is multiplied by LVET and a volume conductor(V_(c)) allometrically related by body mass to the intrathoracic bloodvolume. As compared to CO measured with TDCO and transesophagealechocardiography, good to high correlation and limits of agreementwithin +/−30% are reported.

While most ICG measurements are conducted on the thorax, there is goodevidence in the literature implying that left ventricular SV can beobtained from the upper extremity, and specifically the brachium. Forexample, Chemla et al. showed that peak aortic blood acceleration ishighly correlated with peak brachial artery blood acceleration (r=0.79)(see, e.g., Chemla et al., Blood flow acceleration in the carotid andbrachial arteries of healthy volunteers: respective contributions ofcardiac performance and local resistance; Fundam Clin Pharmacol; 10:393-399 (1996)). This study also demonstrated that, while brachial bloodvelocity is affected by downstream vasoactivity, peak brachial bloodacceleration is solely affected by the upstream β-adrenergic influencesof cardiac impulse formation. This suggests that square roottransformation of brachial (dZ/dt)_(max)/Z_(o) may yield accurateestimations of SV when multiplied by LVET and a Vc of appropriatemagnitude. Stanley et al. showed that the maximum early systolic upslopeof the transthoracic and brachial impedance changes (ΔZ) are identical,indicating that they are linearly correlated (see, e.g. Stanley et al.,Multi-channel electrical bioimpedance: a new noninvasive method tosimultaneously measure cardiac and peripheral blood flow; J Clin MonitComput; 21: 345-51 (2007)). This implies that, despite being ofdifferent magnitudes, the peak rate of change of the trans-thoracic andtrans-brachial impedance changes can both be used to calculate SV.Finally, Wang et al. demonstrated that impedance changes (ΔZ(t)) in theforearm are highly correlated with Doppler-derived SV, showing acorrelation coefficient of r=0.86 (see, e.g. Wang et al., Evaluation ofchanges in cardiac output from the electrical impedance waveform in theforearm; Physiol Meas; 28: 989-999 (2007)).

CO/SV can also be estimated from a time-dependent arterial bloodpressure waveform measured, e.g., with a tonometer or in-dwellingarterial catheter. Algorithms can be used to extract pulse pressure (PP)and other contour-related features from these waveforms, which are thenprocessed to estimate CO/SV. Unfortunately both the heart and itsassociated vessels can function independently and sometimesparadoxically, so changes in parameters like PP may both reflect andmask changes in CO/SV. In other words, measurements of CO usingtime-dependent arterial waveforms represent a combination of cardiac andvascular function.

Pulse arrival time (PAT), defined as the transit time for a pressurepulse launched by a heartbeat in a patient's arterial system, has beenshown in a number of studies to correlate to both systolic (SYS) anddiastolic (DIA) blood pressures. In these studies, PAT is typicallymeasured with a conventional vital signs monitor that includes separatemodules to determine both an electrocardiogram (ECG) and a value forpulse oximetry (SpO2). During a PAT measurement, multiple electrodestypically attach to a patient's chest to determine a time-dependentcomponent of the ECG waveform characterized by a sharp spike called the‘QRS complex’. The QRS complex indicates an initial depolarization ofventricles within the heart and, informally, marks the beginning of theheartbeat and a pressure pulse that follows. SpO2 is typically measuredwith a bandage or clothespin-shaped sensor that attaches to a patient'sfinger, and includes optical systems operating in both red and infraredspectral regions. A photodetector measures radiation emitted from theoptical systems that transmits through the patient's finger. Other bodysites, e.g., the ear, forehead, and nose, can also be used in place ofthe finger. During a measurement, a microprocessor analyses both red andinfrared radiation measured by the photodetector to determinetime-dependent waveforms corresponding to the different wavelengths,each called a photoplethysmogram waveform (PPG). From these a SpO2 valueis calculated Time-dependent features of the PPG waveform indicate bothpulse rate and a volumetric absorbance change in an underlying artery(e.g., in the finger) caused by the propagating pressure pulse.

Typical PAT measurements determine the time separating a maximum pointon the QRS complex (indicating the peak of ventricular depolarization)and a portion of the PPG waveform (indicating the arrival of thepressure pulse). PAT depends primarily on arterial compliance, thepropagation distance of the pressure pulse (which is closelyapproximated by the patient's arm length), and blood pressure. Toaccount for patient-specific properties, such as arterial compliance,PAT-based measurements of blood pressure are typically ‘calibrated’using a conventional blood pressure cuff. Typically during thecalibration process the blood pressure cuff is applied to the patient,used to make one or more blood pressure measurements, and then removed.Going forward, the calibration measurements are used, along with achange in PAT, to determine the patient's blood pressure and bloodpressure variability. PAT typically relates inversely to blood pressure,i.e., a decrease in PAT indicates an increase in blood pressure.

A number of issued U.S. patents describe the relationship between PATand blood pressure. For example, U.S. Pat. Nos. 5,316,008; 5,857,975;5,865,755; and 5,649,543 each describe an apparatus that includesconventional sensors that measure ECG and PPG waveforms, which are thenprocessed to determine PAT.

SUMMARY OF THE INVENTION

The invention provides a small-scale, body-worn monitor for measuringSV/CO/CP, along with cNIBP, HR, respiratory rate (RR), SpO2, and bodytemperature (TEMP), motion, and posture. Measurements of CO/SV are basedon a measurement technique called ‘transbrachial electro-velocimetry’(TBEV), which is described in detail below. TBEV measurements yield twotime-dependent waveforms: Zo, which represents a base impedance in thebrachial region, and is sensitive to slowly varying properties such asblood volume; and ΔZ(t), which features heartbeat-induced pulses thatvary in contour as blood flows through the brachium during both systoleand diastole. These waveforms are measured from the patient's brachium,a region that is somewhat immune to pulmonary ventilatory affects thatcan complicate conventional ICG measurements obtained from the thorax.Collectively, an algorithm running on a microprocessor within thebody-worn monitor analyzes features analysis of both Zo and ΔZ(t) todetermine values for each TBEV measurement. More specifically, todetermine SV/CO/CP values, the monitor relies on a ‘hybrid measurement’that collectively processes combinations of time-dependent PPG, ECG, andTBEV waveforms, along with physiological parameters (e.g. blood pressurevalues) extracted from these waveforms, measured by the body-wornmonitor. From these waveforms parameters such as LVET and PEP can beestimated and used in a mathematical relationship to continuously andaccurately estimate SV/CO/CP values, as described in detail below. Oncedetermined, they are combined with conventional vital signs, andwirelessly transmitted by the body-worn monitor to a central station toeffectively monitor the patient.

The TBEV waveform is measured with a small module that connects to afirst set of adhesive electrodes worn in the patient's clavicle/brachial(CB) region. This region roughly extends from areas near the tip of theshoulder (proximal to the axilla) to the elbow (proximal to theantecubital fossa). ECG waveforms are measured with a small module thatconnects to second set of adhesive electrodes that are typically worn onthe patient's thorax in a conventional Einthoven's triangleconfiguration. Both the TBEV and ECG modules also include a 3-axisaccelerometer that measures acceleration waveforms (ACC) that aresensitive to motion. Both accelerometers measure, for example,breathing-induced chest wall excursions that can be processed toestimate RR, as well as larger scale motion that can be processed todetermine motion-related properties such as activity level, posture,degree/magnitude of motion, and frequency of motion.

During a measurement, the TBEV and ECG modules transmit waveforms andnumerical information through either a wired or wireless connection to awrist-worn transceiver. The transceiver also connects to an opticalsensor, worn on the patient's thumb, that measures PPG waveformsgenerated with optical systems featuring red (˜600 nm) and infrared(˜900 nm) light-emitting diodes (LEDs). These waveforms can be processedto determine values of SpO2. The wrist-worn transceiver also includes aninternal accelerometer that measures ACC waveforms associated with handmotions. Both PPG waveforms, along with the ECG waveforms, can beprocessed to determine cNIBP values.

The TBEV component of the hybrid measurement is measured by injecting ahigh-frequency, low-amperage alternating current (AC) field along thecourse of the brachial artery in the CB region, followed bysimultaneously sensing and signal processing voltage changes producedwithin the current field. The fundamental rational for TBEV derives fromthe direct proportionality and high correlation observed between peakascending aortic and peak brachial artery blood flow acceleration. Thistechnique is in diametric opposition to the generally acceptedvolumetric theory, an alternative approach that suggests it is thevelocity-induced peak rate of change in the specific resistance ofaxially-directed flowing blood that causes a time-dependent change inthe measured impedance. Computationally, TBEV-determined SV is obtainedby taking the square root of the peak rate of change of electricalimpedance pulse variation divided by the base impedance as measured inthe CB region, i.e. [(dZ/dt)_(max)/Zo]^(0.5). This parameter is thenmultiplied by LVET and a constant V_(c) to yield SV.

The body-worn monitor simultaneously provides a technique for measuringcNIBP, based on either PAT, pulse transit time (PTT) or vascular transittime (VTT), as described in more detail in the above-referenced patentapplications. These documents describe cNIBP measurements made using the‘Composite Method’, described in detail below, which features a numberof improvements over conventional PAT and PTT measurements.

Upon completion of a measurement, the body-worn monitor wirelesslytransmits waveforms, vital signs, and SV/CO/CP values to a remotemonitor, such as a personal computer (PC), workstation at a nursingstation, tablet computer, personal digital assistant (PDA), or cellulartelephone. Typically the wireless transmitter is within the wrist-worntransceiver, which also displays and further analyzes this information.Both the remote monitor and the wrist-worn transceiver can additionallyinclude a barcode scanner, touch screen display, camera, voice andspeaker system, and wireless systems that operate with both local-areanetworks (e.g. 802.11 or ‘WiFi’ networks) and wide-area networks (e.g.the Sprint network).

In one aspect, for example, the invention provides a system formeasuring both SV and CO from a patient. The system features animpedance sensor, connected to at least two patient-worn electrodes, andfeaturing an impedance circuit that processes signals from the at leasttwo electrodes to measure an impedance signal from the patient. Anoptical sensor within the system connects to an optical probe, andfeatures an optical circuit that measures at least one optical signalfrom the patient. A body-worn processing system operably connects toboth the impedance sensor and the optical sensor and receives andprocesses the impedance signal to determine a first value of SV and CO.It then receives the optical signal and processes it to determine asecond value of these parameters. Finally, the processing systemcollectively processes both the first and second values of SV and CO todetermine a third value of these parameters, which it then reports to adisplay device.

In another aspect, the invention provides a similar system that alsofeatures an ECG sensor, connected to at least two body-worn electrodes,and featuring an ECG circuit. The ECG circuit is configured to processsignals from the electrodes to measure an ECG waveform and HR value. Aprocessing system connects to the impedance, optical, and ECG sensors,and receives time-dependent waveforms from each of these systems. Itthen collectively processes the ECG and optical signals to determine ablood pressure value, and then processes the blood pressure value toestimate SV and CO.

In another aspect, the invention provides a similar system that featuresECG, impedance, and optical sensors. Collectively these sensors generatesignals that are processed to determine a collection of SV ‘estimators’.The various estimators are then processed with a variety of algorithmsto estimate stroke volume.

In another aspect the invention provides a method for determining SVthat features the following steps: (a) measuring an impedance signalwith an impedance sensor operably connected to the body-worn monitor;(b) measuring an optical signal with an optical sensor; (c) processingthe impedance signal to determine a value of (dZ/dt)_(max); (d)processing the optical signal to determine a value of SFT; and (e)collectively processing Z_(o), (dZ/dt)_(max) and SFT to determine theSV.

In another aspect, the invention provides a method of determiningcardiac power, which as described in detail below is the product of COand MAP. Here, CO is determined by processing an ECG waveform todetermine a heart rate value, and a combination of impedance and opticalwaveforms to determine SV. MAP is then calculated from a PAT valuedetermined from PPG and ECG waveforms. Alternatively, MAP is calculatedfrom a VTT value determined from TBEV and PPG waveforms. In both cases,the Composite Method processes either PAT or PTT to determine MAP.

In another aspect, the invention provides a body-worn system formeasuring a SV value from a patient. The body-worn system features aTBEV module that includes an electrical circuit configured to inject acurrent proximal to the patient's brachium. The circuit's bottom portionincludes a pair of electrical connectors that are configured to snapinto a pair of mated connectors disposed on a first electrode, with afirst connector configured to inject the current into a first portion ofthe electrode, and a second connector configured to measure signals froma second portion of the electrode that relate to a voltage. An analogcircuit then processes the signals from the second connector to generatea voltage value. A processor interfaced to the analog circuit convertsthe voltage value, or a value calculated therefrom, into atime-dependent resistance value, and then converts the time-dependentresistance value into a SV value.

In yet another aspect, the invention provides a method for determiningSV from a patient that includes the following steps: (a) measuring amotion waveform with a first motion sensor; (b) processing the motionwaveform with a motion algorithm to determine a motion-relatedparameter; (c) comparing the motion-related parameter to apre-determined threshold parameter to determine if the patient's motionexceeds an acceptable level; (d) measuring an impedance waveform fromthe patient with an impedance sensor if the patient's motion does notexceed an acceptable level; and (e) calculating a SV value from theimpedance waveform if the patient's motion does not exceed an acceptablelevel.

The Composite Method for cNIBP is described in detail in the followingpatent application, the contents of which are fully incorporated hereinby reference: BODY-WORN SYSTEM FOR MEASURING CONTINUOUS NON-INVASIVEBLOOD PRESSURE (cNIBP), U.S. Ser. No. 12/650,354, filed Nov. 15, 2009.It includes both pressure-dependent and pressure-free measurements, andis based on the discovery that PAT and the PPG waveform used todetermine it are strongly modulated by an applied pressure. During apressure-dependent measurement, also referred to herein as an ‘indexingmeasurement’, two events occur as the pressure gradually increases tothe patient's systolic pressure: 1) PAT increases, typically in anon-linear manner, once the applied pressure exceeds diastolic pressure;and 2) the magnitude of the PPG's amplitude systematically decreases,typically in a linear manner, as the applied pressure approachessystolic pressure. The applied pressure gradually decreases blood flowand consequent blood pressure in the patient's arm, and thereforeinduces the pressure-dependent increase in PAT. Each of the resultingpairs of PAT/blood pressure readings measured during the period ofapplied pressure can be used as a calibration point. Moreover, when theapplied pressure equals SYS, the amplitude of the PPG waveform iscompletely eliminated, and PAT is no longer measurable. Collectivelyanalyzing both PAT and the PPG waveform's amplitude over a suitablerange, along with the pressure waveform using techniques borrowed fromconventional oscillometry, yields the patient's SYS, DIA, and MAP, alongwith a patient-specific slope relating PAT and MAP. From theseparameters the patient's cNIBP can be determined without using aconventional cuff.

A combination of several algorithmic features improves the efficacy ofthe Composite Method over conventional PAT measurements of cNIBP. Forexample, sophisticated, real-time digital filtering removeshigh-frequency noise from the PPG waveform, allowing its onset point tobe accurately detected. When processed along with the ECG waveform, thisensures measurement of an accurate PAT and, ultimately, cNIBP value. Thepressure-dependent indexing method, which is made during inflation ofthe arm-worn cuff, yields multiple data points relating PAT and bloodpressure during a short (˜60 second) measurement. Processing of thesedata points yields an accurate patient-specific slope relating PAT tocNIBP. Inclusion of multiple accelerometers yields a variety of signalsthat can determine features like arm height, motion, activity level, andposture that can be further processed to improve accuracy of the cNIBPcalculation, and additionally allow it to be performed in the presenceof motion artifacts. And a model based on femoral blood pressure, whichis more representative of pressure in the patient's core, can reduceeffects such as ‘pulse pressure amplification’ that can elevate bloodpressure measured at a patient's extremities.

The Composite Method can also include an ‘intermediate’pressure-dependent measurement wherein the cuff is partially inflated.This partially decreases the amplitude of the PPG waveform in atime-dependent manner. The amplitude's pressure-dependent decrease canthen be ‘fit’ with a numerical function to estimate the pressure atwhich the amplitude completely disappears, indicating systolic pressure.

For the pressure-dependent measurement, a small pneumatic systemattached to the cuff inflates the bladder to apply pressure to anunderlying artery according to the pressure waveform. The cuff istypically located on the patient's upper arm, proximal to the brachialartery, and time-dependent pressure is measured by an internal pressuresensor, such as an in-line Wheatstone bridge or strain gauge, within thepneumatic system. The pressure waveform gradually ramps up in a mostlylinear manner during inflation, and then slowly rapidly deflates througha ‘bleeder valve’ during deflation. During inflation, mechanicalpulsations corresponding to the patient's heartbeats couple into thebladder as the applied pressure approaches DIA. The mechanicalpulsations modulate the pressure waveform so that it includes a seriesof time-dependent oscillations. The oscillations are similar to thosemeasured with an automated blood pressure cuff using oscillometry, onlythey are measured during inflation rather than deflation. They areprocessed as described below to determine a ‘processed pressurewaveform’, from which MAP is determined directly, and SYS and DIA aredetermined indirectly.

Pressure-dependent measurements performed on inflation have severaladvantages to similar measurements performed on deflation, which areconvention. For example, inflation-based measurements are relativelyfast and comfortable compared to those made on deflation. Mostconventional cuff-based systems using deflation-based oscillometry takeroughly four times longer than the Composite Method's pressure-dependentmeasurement. Inflation-based measurements are possible because of theComposite Method's relatively slow inflation speed (typically 5-10mmHg/second) and high sensitivity of the pressure sensor used within thebody-worn monitor. Moreover, measurements made during inflation can beimmediately terminated once systolic blood pressure is calculated. Incontrast, conventional cuff-based measurements made during deflationtypically apply a pressure that far exceeds the patient's systolic bloodpressure; pressure within the cuff then slowly bleeds down below DIA tocomplete the measurement.

Pressure-free measurements immediately follow the pressure-dependentmeasurements, and are typically made by determining PAT with the sameoptical and electrical sensors used in the pressure-dependentmeasurements. Specifically, the body-worn monitor processes PAT andother properties of the PPG waveform, along with the patient-specificslope and measurements of SYS, DIA, and MAP made during thepressure-dependent measurement, to determine cNIBP.

The invention in general, and particularly the hybrid measurement forSV/CO/CP, features many advantages over conventional techniques used tomeasure these properties. Compared to TDCO and Fick, for example, thebody-worn monitor facilitates continuous, noninvasive measurement ofthese values that is highly accurate and has a low-risk of detrimentalcomplications, such as infection and pulmonary artery vesselperforation. And unlike measurements based on TDCO, Fick, and Doppler,the hybrid measurement does not require a specially trained observer.TBEV measurements are performed at the brachium, which by itself hasseveral advantages over conventional ICG measurements made from thethorax. For example, complications in the pulmonary system, i.e.intra-thoracic liquids and pulmonary edema, do not affect SV/CO/CPvalues measured from this region. Similarly, the baseline trans-brachialquasi-static impedance, Zo is not affected by medical equipmentsometimes present in the thorax, such as chest tubes, external pacemakerwires, and central venous lines. Typically thoracic ICG measurementsrequire 8 separate electrodes, whereas the TBEV measurement describedherein only requires 2 separate electrodes. Finally, without theinfluence of pulmonary ventilation and pulmonary artery pulsations, thesignal-to-noise ratio of waveforms measured from the brachium isrelatively high.

These and other advantages of the invention will be apparent for thefollowing detailed description, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a schematic drawing of an algorithm that performs thehybrid measurement for determining SV/CO/CP values according to theinvention;

FIG. 2 shows a schematic drawing of the custom electrode patch used toperform TBEV measurements described in FIG. 1;

FIG. 3 shows a schematic drawing of the human body's circulatory systemindicating regions where both conventional ICG and TBEV measurements aremade;

FIG. 4 shows a schematic diagram showing the body-worn monitor of theinvention positioned relative to both the aorta and brachial arteries,and how these vessels change in diameter during systole and diastole;

FIG. 5 shows a schematic drawing of a body-worn monitor that performsthe hybrid measurement shown in FIG. 1;

FIG. 6A shows a schematic drawing of an alternate embodiment of thebody-worn monitor that performs the hybrid measurement shown in FIG. 1;

FIG. 6B shows a three-dimensional drawing of the TBEV module and customelectrode used in the alternate embodiment shown in FIG. 6A;

FIG. 7A shows a schematic diagram of analog and digital circuits used tomake a TBEV measurement according to the invention; FIG. 7B shows a TBEVwaveform and a highpass filtered portion thereof;

FIG. 8 shows a flow chart of an algorithm for calculating SV wheresystolic flow time (SFT) is determined using Weissler's regression;

FIG. 9 shows a flow chart of an algorithm for calculating SV using theSV estimators shown in FIG. 1;

FIG. 10 shows a three-dimensional image of a wrist-worn transceiver,which is part of the body-worn monitor shown in FIGS. 5 and 6A;

FIGS. 11A-E show time-dependent plots of, respectively, ECG, ICG, TBEV,d(ICG)/dt, and d(TBEV)/dt waveforms measured with the body-worn monitorof FIG. 5;

FIGS. 12A-B show time-dependent plots of dZ/dt waveform measured from,respectively, the thorax and brachium;

FIGS. 13A-13D show time-dependent plots of, respectively, a TBEVwaveform, a derivative of the TBEV waveform showing fiducial points neara pulse maximum used to determine the pulse's onset, a derivative of theTBEV waveform showing a shaded region indicating where Weissler'sregression is used to estimate LVET, and a derivative of the TBEVwaveform where SFT is determined;

FIG. 14 shows a correlation plot comparing SFT measured using a ‘fused’approach that relies partially on HR, pulse rate (PR) and Weissler'sregression, and LVET measured with Doppler/ultrasound;

FIGS. 15A-E show two-dimensional Doppler/ultrasound images of thebrachial artery measured during systole (FIGS. 15A-C), and diastole(FIGS. 15D, E);

FIG. 15F shows a time-dependent plot of an ECG waveform and a TBEVwaveform that corresponds to the two-dimensional images shown in FIGS.15A-E;

FIGS. 16A, B show time-dependent plots of, respectively, a waveformextracted from Doppler/ultrasound images taken from the brachium,similar to those shown in FIGS. 15A-E, and a simultaneously measuredTBEV waveform;

FIG. 16C, D show plots of, respectively, the time-dependent derivativesof the waveforms shown in FIGS. 16A, B;

FIG. 17 shows derivatized TBEV waveforms measured from 7 uniquesubjects;

FIG. 18A, B show, respectively, correlation and Bland-Altman plotscomparing CO measured from of 23 subjects using Doppler/ultrasound andTBEV;

FIGS. 19A, B show time-dependent plots of, respectively, ICG and TBEVwaveforms corresponding to high and low values of CO measured while asubject is wearing military anti-shock trousers (MAST);

FIGS. 20A-C show time-dependent plots of, respectively, an unfilteredTBEV waveform showing both cardiac and respiratory events, a TBEVwaveform filtered with a 0.5 to 15 Hz band-pass filter showing onlycardiac events, and a TBEV waveform filtered with a 0.001 to 1 Hzband-pass filter showing only respiration events;

FIGS. 21A, B show time-dependent plots of, respectively, TBEV and ACCwaveforms modulated by respiration events;

FIGS. 21C, D show frequency-domain power spectra of the time-dependentplots shown, respectively, in FIGS. 21A, B;

FIG. 22A shows a time-dependent plot of TBEV and ECG waveforms measuredduring a period of no motion;

FIG. 22B shows a time-dependent plot of ACC waveforms, measuredsimultaneously with the TBEV and ECG waveforms of FIG. 22A, during theperiod of no motion;

FIG. 23A shows time-dependent plots of TBEV and ECG waveforms measuredduring a period of motion;

FIG. 23B shows a time-dependent plot showing ACC waveforms, measuredsimultaneously with the TBEV and ECG waveforms of FIG. 22A, during theperiod of motion;

FIG. 24 shows a schematic drawing of a patient and an overlyingcoordinate axis used with an algorithm and ACC waveforms to determinethe patient's posture;

FIG. 25A shows a plot of time-dependent ACC waveforms measured from apatient's chest during different postures;

FIG. 25B shows a plot of time-dependent postures determined byprocessing the ACC waveforms of FIG. 25A with an algorithm andcoordinate axis shown in FIG. 24;

FIG. 26 shows a schematic drawing of ECG and PPG waveforms, and how thePAT determined from these waveforms and the contours of the PPGwaveforms can be collectively analyzed to determine an estimator for SV;

FIG. 27 shows a three-dimensional drawing of a harness used to make aTBEV measurement according to an alternate embodiment of the invention;

FIG. 28 shows a schematic drawing of an alternate embodiment of theinvention wherein the TBEV circuit is contained within a module attacheddirectly to the brachium and detached from the ECG circuit, which isworn on the chest;

FIG. 29 shows a schematic drawing of the TBEV module shown in FIG. 28;

FIG. 30 shows a schematic drawing of an alternate embodiment of theinvention wherein the body-worn monitor wirelessly transmits informationbetween a chest-worn module and the wrist-worn transceiver, and fromthere to a remote monitor; and,

FIG. 31 shows a schematic drawing of the body-worn monitor, similar tothat shown in FIG. 30, that wirelessly transmits information betweenboth a chest-worn module and the wrist-worn transceiver to the remotemonitor.

DETAILED DESCRIPTION OF THE INVENTION

Measurement Overview

Referring to FIG. 1, the invention described herein features a body-wornmonitor that continuously and non-invasively determines SV from TBEVmeasurements 7 collected from a patient's CB region, along with a seriesof SV ‘estimators’ 1-6 made calculated from cNIBP measurements. Thebody-worn monitor is described, for example, in the following patentapplications, the contents of which are incorporated herein byreference: BODY-WORN VITAL SIGN MONITOR, U.S. Ser. No. 12/560,077, filedSep. 15, 2009; and BODY-WORN VITAL SIGN MONITOR, U.S. Ser. No.12/762,726, filed Apr. 19, 2009. SV measurements made using TBEV 7 andthe estimators 1-6 can be incorporated into a ‘hybrid measurement’ 10,operating on a microprocessor within the body-worn monitor, thatdetermines SV and ultimately CO and CP.

TBEV is a variation of conventional bioimpedance techniques, such asICG, and measures waveforms from the CB region to determinetime-dependent parameters such as systolic flow time (SFT),(dZ/dt)_(max), and Zo. These parameters feed into Eq. 3, below, wherethey are coupled with a static parameter V_(c) to determine SV.

$\begin{matrix}{{S\; V} = {V_{C}\sqrt{\frac{\left( {{\mathbb{d}Z}/{\mathbb{d}t}} \right)_{\max}}{Z_{0}}}S\; F\; T}} & (3)\end{matrix}$Here, SV is obtained by taking the square root of the peak rate ofchange of each TBEV pulse divided by the transbrachial base impedance,Zo. This parameter is then multiplied by SFT and a constant-magnitudeV_(c) to yield SV. The derivation of Eq. 3 is described in detail inU.S. Pat. No. 6,511,438 and in the following reference, the contents ofwhich are fully incorporated herein by reference: Bernstein et al.,Stroke Volume Obtained By Electrical Interrogation of the BrachialArtery: Transbrachial Electrical Bioimpedance Velocimetry. Unpublishedmanuscript, submitted 2012. Eq. 3 assumes that (dZ/dt)_(max)/Z_(o)represents a dimensionless acceleration of blood (with units of 1/s²),which is the ohmic analog of peak aortic blood acceleration (cm/s²).Forceful systolic ejection of blood from the left ventricle of the heartaligns the erythrocytes in parallel during systolic flow to generate apulsatile increase in conductivity. For this model, V_(c) is estimatedentirely from weight, and is independent of any factors that depend onelectrode separation.

Along with SV, the body-worn monitor simultaneously measures cNIBPvalues (SYS, DIA, MAP, and PP) using a cuffless technique called the‘Composite Method’, which is described in detail above. According to thehybrid method, SV is determined explicitly from the TBEV waveforms, andcan be estimated from the cNIBP values. From these parameters multipleestimators 1-7 are determined which the algorithm 10 collectivelyprocesses to determine SV. Additionally, the body-worn monitor featuresmultiple accelerometers that generate time-dependent ACC waveforms,which are then further processed by a motion algorithm 11 to estimatethe patient's level of motion. A function for filtering and rejectingbad data 8 processes information from both the estimators 1-7 and themotion algorithm 11 to determine a collection of valid data points,which are then linearly combined with another function 9 to determinefinal values of SV. The valid data points, for example, are relativelyuncorrupted by motion artifacts; they are determined when the motionalgorithm 11 compares a parameter extracted from an ACC waveform to apre-determined ‘motion threshold’ value. If the parameter exceeds thepre-determined threshold value, the function 8 rejects the correspondingSV values. On the other hand, if the parameter is lower than thepre-determined threshold value, the function 8 approves thecorresponding SV value, and it is passed into the linear combinationalgorithm 9, where it will be processed to determine a final value forSV.

Different threshold values can be applied for SV calculated from TBEV 7,a measurement that is particularly sensitive to motion, and SV estimatedfrom estimators related to blood pressure 1-6, which are less sensitiveto motion. For example, the motion algorithm 11 may determine that asmall amount of motion is present, and thus the linear combinationalgorithm 9 relies completely on SV values determined from theestimators related to blood pressure 1-6. Or it may determine that alarge amount of motion is present, and in response the linearcombination algorithm 9 will not report an SV value. If the motionalgorithm 11 determines that no motion is present, the linearcombination algorithm 9 typically reports a SV value determined entirelyfrom TBEV 7.

In embodiments, the linear combination algorithm 9 combines differentestimators using a simple average or weighted average to determine asingle value of SV. More sophisticated approaches can also be used toprocess the estimators. For example, specific estimators can be selectedbased on a patient's physiological condition or biometric parameters,e.g. their age, gender, weight, or height.

Once SV is determined, it can be further processed as defined in Eqs. 1,2 to determine both CO and CP.

Upon completion of a measurement, the body-worn monitor wirelesslytransmits SV/CO/CP values, along with conventional vital signs, to aremote processing system. For example, these data may flow through ahospital-based wireless network to a central computer interfaced to anelectronic medical records system. From there, medical professionals,such as doctors, nurses, and first responders, can evaluate aconstellation of physiological values corresponding to the patient tomake a diagnosis. Typically, patients wear the body-worn monitor as theytransition from the ambulance, into the hospital, and ultimately to thehome.

A TBEV measurement, described in detail below, injects a low-amperage,high-frequency current into the patient's CB region, and monitors avoltage which relates to the time-dependent resistance encountered bythe current through Ohm's Law (V=I×R). It is based on the assumptionthat the brachial artery, which is the only major artery in the CBregion, undergoes little volumetric expansion during systole, and thuschanges in resistance are due exclusively to acceleration-inducedalignment of erythrocytes within this artery. Stated another way, asblood flows through the artery with each heartbeat, the diameter of thebrachial artery stays relatively constant, but acceleration of the bloodcauses the erythrocytes to align. This physiological processconsequently increases conductivity, and decreases resistance, in theartery. The time-dependent resistance in the artery is manifested as afirst waveform, called ΔZ(t), which features a series of pulses, eachcorresponding to a unique heartbeat. A second TBEV waveform, Zo, isfiltered to only reflect the baseline impedance of the artery, and issensitive to relatively low-frequency processes, such as blood volume,interstitial fluids, and occasionally respiration rate.

Estimators for determining SV from blood pressure values include theLilijestrand 1, Wesseling 2, MAP 3, and Herd 4 estimators. These dependlinearly on blood pressure values, and are shown below in Table 1. Inthis table SYS_(area) refers to the area under the PPG waveform duringsystole, ΔP_(a) is the beat-to-beat blood pressure change, T_(n) is theduration of the cardiac cycle, and τ_(n) is a time constant that governsthe intra-cycle dynamics of the Windkessel model.

These estimators are summarized in detail in the following reference,among other places, the contents of which are incorporated herein byreference: Chen, Cardiac Output Estimation from Arterial Blood PressureWaveforms using the MIMIC II Database; Thesis for Masters Degreesubmitted to the Massachusetts Institute of Technology; (2009); andParlikar et al., Model-Based Estimation of Cardiac Output and TotalPeripheral Resistance; unpublished manuscript available athttp://icp.mit.edu/pdf/Parlikar07.pdf. Estimators based on bloodpressure can be determined using the Composite Method, or alternativelywith a conventional cuff-based method, such as oscillometry orauscultation.

TABLE 1 Estimators for CO CO ESTIMATOR CO FORMULA (CO = k * equationbelow) Lilijestrand [(PP/(SYS + DIA)] * HR Wesseling (163 + HR − 0.48 *MAP) * SYS_(area) * HR MAP MAP SYS SYS_(area) * HR ParlikarHeldt(ΔP_(n)/T_(n) + MAP/τ_(n)) Herd (MAP − DIA) * HR Windkessel PP * HR

Other SV estimators that can be processed by the algorithm include thosebased on PAT 5, which is determined using PPG and ECG waveforms measuredby the body-worn monitor, and is described in the following reference,the contents of which are incorporated by reference: Wang et al., Thenon-invasive and continuous estimation of cardiac output using aphotoplethysmogram and electrocardiogram during incremental exercise;Physiol. Meas.; 31: 715-726 (2010). FIG. 26 and Eq. 4, below, indicateWang's methodology for analyzing PAT, ECG 375, and PPG 380 waveforms todetermine a relative value of CO.CO=D×[C−ln(PAT)]×(1−IPA)×(1+IPA)⁻¹  (4)

In the equation D and C are constants defined in the Wang reference, andIPA is shown schematically in FIG. 26. This theory assumes that the PPGwaveform 380 includes a well-defined dichrotic notch allowing theparameters X₁, X₂, and ultimately IPA to be determined. Integration ofan area under the PPG before the notch yields X₁, while integration ofan area under the PPG after the notch yields X₂. IPA is defined as theratio of X₁ to X₂. Once determined, PAT and IPA are used in Eq. 4 toyield another estimator of SV/CO/CP.

The PPG waveform, taken by itself, can be analyzed and used as an‘other’ estimator 6 for algorithm 10. This waveform represents atime-dependent volumetric expansion of the underlying artery from whichit is measured, and is thus different than a traditional cNIBP waveform,such as that measured using an in-dwelling arterial catheter, whichrepresents the time-dependent pressure in the artery. However, PPG andcNIBP waveforms share a similar morphology, particularly over relativelylong time periods, and can be analyzed to estimate both blood flowdynamics and hence SV. The following reference, the contents of whichare incorporated herein by reference, describes an analysis method forprocessing waveforms to extract these parameters: Lu et al., Continuouscardiac output monitoring in humans by invasive and noninvasiveperipheral blood pressure waveform analysis; J Appl Physiol 101: 598-608(2006).

In still other embodiments, an ‘other’ estimator 6 for the SV/CO/CPmeasurement can be based on a measurement technique performed by anexternal sensor that connects to the body-worn monitor. Such aconnection can be made using either wired or wireless means. Forexample, a technique such as near-infrared spectroscopy (NIRS) can beused to estimate SV as described in the following references, thecontents of which are incorporated herein by reference: Soller et al.,Noninvasively determined muscle oxygen saturation is an early indicatorof central hypovolemia in humans; J Appl Physiol 104: 475-481 (2008). Asensor incorporating a NIRS measurement can thus be integrated with thebody-worn monitor and attached to the patient's body during ameasurement. Values for SV calculated with this sensor are sent to themonitor through the wired or wireless connection, and can beincorporated in the algorithm 10 to further improve the accuracy of thecontinuous, non-invasive determination of SV. In all cases, thecollection of estimators 1-6 relate to CO through a calibration factor(k in Table 1) determined from an absolute measurement of SV, which inthe algorithm 10 is provided by the TBEV measurement 7. Typically TBEVdetermines SV to within about +/−20%. Perhaps more importantly, theestimators 1-6 and TBEV measurement 7 determine SV with completelydifferent methodologies and from different locations on the body. Thusit is possible that combining the measurements into a single algorithm10 may reduce error caused by well-known physiological effects that aretypically isolated to these locations.

As shown in FIG. 2, TBEV measurements are typically made with a pair ofcustom electrodes 24, each featuring two conductive regions 45, 47. Theouter conductive region 45 (i.e. the region furthest removed from the CBregion) of each electrode 24 injects a low-amperage (<5 mA), highfrequency (50-100 kHz) current into the patient's CB region. The innerconductive region 47 then measures the time-dependent voltage across theartery. As described above, variations in this voltage are due toresistance changes caused by blood flowing through the brachial artery,and more specifically due to acceleration-induced alignment oferythrocytes occurring with each heartbeat. This physiology provides abasis for the mathematical model shown above in Eq. 3.

Each conductive region 45 and 47 typically consists of a conductive‘liquid gel’ material that roughly matches the impedance properties ofhuman skin. The liquid gel is deposited on top of a conductive substratecoated with a large-area Ag:AgCl film that, in turn, is deposited on topof a flexible substrate 23. The liquid gel, for example, can be asponge-like material saturated with a conductive gel or fluid. Theneighboring conductive regions 45, 47 are electrically isolated fromeach other, and individually connect through a pair of individualconductive traces 54A, B to a pair of electrical leads 52A, B adhered tothe flexible substrate 23. The electrical leads 52A, B, for example, canbe metal rivets or posts that easily snap into a corresponding femaleconnector. An insulating adhesive layer (not shown in the figure)dispersed between the conductive regions 45 and 47 electrically isolatesthese portions of the electrode 24, and is coated with an adhesive thatenables it to be securely attached to the patient during a measurement.

FIGS. 3 and 4 indicate some of the advantages of TBEV measurements,which are made in the CB region 58 of the patient 20, as compared toconventional ICG measurements, which are made in the thorax 59. As isclear from FIG. 3 (with the underlying image borrowed from Gray'sAnatomy), the thorax 59 features a vast and complicated collection ofarteries and veins, as well as most of the patient's vital organs, suchas their lungs, heart, kidneys, liver, stomach, and gastro-intestinaltrack. Each of these systems, and most particularly the lungs and largearteries stemming from the left side of the heart, contain conductivefluids (e.g. blood and lung fluids) that will influence conventional ICGwaveforms. For example, physiological processes such as excess lungfluids, pulmonary edema, and pulmonary injury can alter thetime-dependent impedance characteristics of the patient's thorax. Thus,the resulting waveforms measured therefrom are no longer reflective ofthe true hemodynamic state. In stark contrast is the brachium 58, whichis physically removed from pulmonary affects and features only one largeartery—the brachial artery—which influences the TBEV measurement.Ultimately this simplifies the morphology of the TBEV waveform andlessens its patient-to-patient variability, thereby simplifying thecalculation of SV.

Importantly, previous studies have indicated a strong correlationbetween peak blood acceleration in the aorta, where the SV is firstmanifested, and peak blood acceleration in the brachium, where TBEVmeasures a signal used to estimate SV by square root transformation.Insofar as velocities are concerned, peak aortic blood velocity isroughly 80-124 cm/s (mean ˜100 cm/s), while that in the brachial arteryis roughly 30-70 cm/s (mean ˜50 cm/s). Experiments that measured theseparameters are described in the following references, the contents ofwhich are incorporated herein by reference: Gardin J M et al.,Evaluation of blood flow velocity in the ascending aorta and mainpulmonary artery of normal subjects by Doppler echocardiograpy. Am.Heart J. 1984; 107:310; Wilson S et al., Normal intracardiac and greatartery blood velocity measurements by pulsed Doppler echocardiography.Br. Heart J. 1985; 53:451; Fronek A., Non invasive diagnostics invascular disease. McGraw-Hill, N.Y. 1989, pp 117; Green D, et al.,Assessment of brachial artery blood flow across the cardiac cycle:retrograde flows during bicycle ergometry. J. Appl. Physiol 2002;93:361. These references indicate that, to a first approximation, theaverage blood velocity in the aorta is roughly twice that in thebrachial artery.

FIG. 4 indicates how TBEV signals are further reduced in complexity ascompared to ICG signals. Without being bound to any theory, this islikely because of the relatively complex time-dependent properties ofvasculature in the thorax (e.g. the aorta 66A, 66B), as compared tothose in the CB region (e.g. the brachium 68A, 68B). More specifically,the figure shows the location of the body-worn monitor 19 on a patient,along with schematic drawings of the patient's aorta 66A, 66B andbrachial artery 68A, 68B. During systole, the left ventricle contractsto force blood into the aorta 66A, with the volume of ejection definedas the SV. This process creates two simultaneous processes in the aortaas the cardiac cycle moves from diastole to systole: 1) a volumetricincrease as the aorta's arterial walls, which are highly elastic andexpand to an enlarged state 66A during systole and then recoil to arelaxed state 66B during diastole; and 2) an acceleration-inducedalignment of erythrocytes within the arterial lumen, causing these cellsto move from a random orientation during diastole to an aligned parallelorientation during systole. Without being bound by any theory, it islikely that both the volumetric and acceleration-induced alignmentprocesses take place in the aorta during the cardiac cycle. Bothprocesses affect the conductivity of blood in the aorta in apatient-specific manner, thereby complicating the pulsatile component ofthe ICG signal, and making it difficult for a single mathematicalequation to characterize a large set of patients. Historicallyparameters extracted from ICG signals are fed into the well-knownSramek-Bernstein equation, shown below in Eq. 5, is based on thevolumetric expansion model:

$\begin{matrix}{{S\; V} = {\delta\;\frac{L^{3}}{4.25}\frac{\left( {{\mathbb{d}Z}/{\mathbb{d}t}} \right)_{\max}}{Z_{0}}L\; V\; E\; T}} & (6)\end{matrix}$In Eq. 5 δ represents compensation for body mass index, Zo is the baseimpedance, and L is estimated from the distance separating thecurrent-injecting and voltage-measuring electrodes on the thorax. Thisequation and several mathematical derivatives are described in detail inthe following reference, the contents of which are incorporated hereinby reference: Bernstein, Impedance cardiography: Pulsatile blood flowand the biophysical and electrodynamic basis for the stroke volumeequations; J Electr Bioimp; 1: 2-17 (2010). Eq. 5 depends on LVET, whichis estimated from each pulse in the ICG waveform, as is described inmore detail below. Both the Sramek-Bernstein Equation and an earlierderivative of this, called the Kubicek Equation, feature a ‘staticcomponent’, Z₀, and a ‘dynamic component’, ΔZ(t), which relates to LVETand a (dZ/dt)_(max)/Z_(o) value, calculated from the derivative of theraw ICG signal, ΔZ(t). These equations assume that (dZ/dt)_(max)/Z_(o)represents a radial velocity (with units of Ω/s) of blood due to volumeexpansion of the aorta.

In contrast to the aorta, the brachial artery is a relatively muscularvessel that undergoes little expansion during systole 68A and recoilduring diastole 68B; its arterial volume, as shown in FIG. 4, thusremains relatively constant during the cardiac cycle. Time-dependentchanges in the arterial waveform are thus due nearly exclusively toperiodic, sinusoidal, heartbeat-induced parallel alignment of theerythrocytes within the artery. Ultimately this means that, to developan underlying mathematical model for the brachial artery, it is notnecessary to estimate the relative contributions of volumetric expansionand erythrocyte alignment, which as described above may vary with eachpatient.

Sensor Configurations

Referring to FIGS. 5 and 6, in a preferred embodiment the body-wornmonitor 19 is distributed on a patient 20 to measure SV/CO/CP. Themonitor features a TBEV module 22, worn near the CB region, which isattached to the patient 20 using a first two-part electrode 24 shown inFIG. 2. A second two-part electrode 28 attaches to the patient 20 nearthe elbow. As described above, the outer conductive area in the firsttwo-part electrode injects a high-frequency, low-amperage current intothe patient's CB region, while the outer conductive area in the secondtwo-part electrode serves as a sink for this current. Simultaneously,the inner electrodes measure a voltage that describes resistanceencountered by the propagating current according to Ohm's Law. Eachelectrode in the first two-part electrode 24 features a rivet or postthat snaps into a mated female component in the TBEV module, therebyconnecting these components directly to analog circuitry therein.Similarly, electrodes in the second two-part electrode 28 connect tosnaps embedded in a cable 26 that connects the first 24 and second 28electrodes. The cable 26 includes conductors for transmitting digitaldata through the control area network (CAN) protocol. Use of thisprotocol is described in detailed in the following patent application,the contents of which have been previously incorporated herein byreference: BODY-WORN VITAL SIGN MONITOR, U.S. Ser. No. 12/560,077, filedSep. 15, 2009. The cable 26 additionally includes conductors fortransmitting analog signals to the TBEV module 22 to measure theabove-described voltage. An analog-to-digital converter (not shown inthe figure) within the module digitizes the TBEV waveforms to form ΔZ(t)and Zo, which are then analyzed with a microprocessor (also not shown inthe figure) as described above to determine a value for SV.

An ECG module 40 worn on the patient's thorax connects to the TBEVmodule 22 through a similar cable 27 that only includes conductors fortransmitting digital signals according to the CAN protocol. The ECGmodule 40 connects to a trio of disposable ECG electrodes 42A-C,disposed on the patient's thorax in a conventional ‘Einthoven'striangle’ configuration, through a corresponding trio of ECG leads44A-C. During a measurement, the ECG module 40 measures analog signalsfrom each electrode 42A-C and lead 44A-C, and performs a differentialamplification of these signals according to known techniques in the artto generate an ECG waveform. An analog-to-digital converter (not shownin the figure) digitizes the ECG waveform, and a microprocessor (alsonot shown in the figure) analyzes the well-known QRS complex within thiswaveform with a beat-picking algorithm to determine a HR value. Digitalrepresentations of these data are sent within CAN-formatted packetsthrough the cable 27 to a CAN transceiver (not shown in the figure)within the TBEV module 22. There, the packets are combined withcorresponding packets that include the TBEV waveform and SV values,which are calculated as described above. These packets pass through CANconductors in the cable 26, past the second two-part electrode 28, andthen through a third cable 29 to a wrist-worn transceiver 30 thatconnects to the patient's wrist using a plastic cradle 32 and Velcrostrap 34. These components are described in more detail in the followingco-pending patent applications, the contents of which have beenpreviously incorporated by reference: BODY-WORN VITAL SIGN MONITOR, U.S.Ser. No. 12/560,077, filed Sep. 15, 2009; and BODY-WORN VITAL SIGNMONITOR, U.S. Ser. No. 12/762,726, filed Apr. 19, 2009. The wrist-worntransceiver 30 additionally connects through a short cable 38 thatcarries only analog signals measured by a thumb-worn optical sensor 36.Within the wrist-worn transceiver is a pulse oximetry circuit (not shownin the figure) that converts signals measured by the optical sensor 36to generate PPG waveforms and corresponding values of SpO2. Amicroprocessor within the wrist-worn transceiver 30 processes PPG andECG waveforms to generate a value of PAT, or alternatively TBEV and PPGwaveforms to generate a value of VTT. These transit times are convertedinto cNIBP values using the Composite Method, as described above. ThecNIBP values, in turn, are converted into SV estimators using thealgorithm shown in FIG. 1. From there, corresponding values of CO aredetermined using SV and ECG-determined values of HR, while values of CPare determined using MAP and CO.

Technically, TBEV-based measurements of SV only require an isolated TBEVwaveform, and can be performed without an ECG waveform. However, thissignal, which is relatively easy to measure and denotes the beginning ofthe cardiac cycle associated with each heartbeat, can be used to ‘gate’the relatively weak TBEV signal to make it easier to extract theproperties described above in Eq. 3. More specifically, theabove-described software beat picker can detect the QRS complex withinthe ECG waveform, which is associated with the onset of an individualheartbeat. Relevant portions of the TBEV waveform typically follow theQRS complex by a few hundred milliseconds. Analysis of these portions ofthe TBEV waveforms yields properties such as SFT, (dZ/dt)_(max), and Zothat are used to calculate SV as described above. Gating the TBEVwaveform in this manner can be particularly effective in analyzing theseproperties when noise is present in the TBEV waveform, e.g. duringperiods of motion.

HR, determined from the ECG waveform, is used to convert SV into CO andCO into CP, as described in Eqs. 1 and 2, above. Typically HR isdetermined from the time period separating neighboring QRS complexes inthe ECG waveform; alternatively it can be estimated from neighboringpulses in either the PPG or TBEV waveform.

The ECG module 40 can additionally connect to 5 leads, and alternatively12 leads. It is typically hard-wired into the TBEV module 22. The thirdcable 29 plugs into the wrist-worn transceiver 30 using a detachableconnector 31 that allows it to be easily removed. In other embodimentsthe order of the ECG module 40 and TBEV module 22 can be reversed sothat the TBEV module 22 is closer to the thorax, and the ECG module 40is closer to the CB region. In still other embodiments, the TBEV module22 can be disposed on the third cable 29 and attach directly to thesecond electrode 28, and the ECG module 40 can be disposed in theoriginal location of the TBEV module 22, and be encapsulated by ahousing that attaches to the first electrode 24 and feeds analog signalsinto the TBEV module 22. In general, multiple configurations of thevarious modules, cables, and electrodes shown in FIG. 6 are within thescope of the invention.

An alternate embodiment of the invention is shown in FIGS. 6A, B. Here,the ECG module 40 is worn on the patient's CB region, and the TBEVmodule 22 is worn near the elbow. Both the ECG 40 and TBEV 22 modulesare attached to the patient with two-part electrodes 24, 28, with theTBEV electrode 24 shown in more detail in FIG. 6B. The two-partelectrode 24 features a pair of female snaps 62A, B disposed on aflexible substrate 64 that connects to underlying conductive regions63A, B. Each conductive region 63A, B features a solid gel material,chosen to match the electrical impedance characteristics of human skin,deposited on a thin Ag/AgCl film. The flexible substrate 64 features anunderlying adhesive layer that during use securely attaches theelectrode 24 to the patient's CB region. The female snaps 62A, B arechosen to geometrically match a pair of metal rivets 61A, B that attachto a bottom portion of a TBEV circuit board 60. During use the rivets61A, B snap into the female snaps 62A, B, thus securing the TBEV module22 to the patient. The rivet 61B furthest away from the CB regionconnects to the TBEV circuit and injects a current through thecorresponding conducting region 63B, while the rivet 61A closest to theregion measures a corresponding voltage. A plastic housing 65 covers theTBEV circuit board 60 and shields it from liquids and other materialspresent in the hospital. Note that in the figure the female snaps 62A, Bare disposed on the electrode 28, and the metal rivets 61A, B aredisposed on the circuit board 60. However in an alternate embodimentthese components can be reversed, i.e. the female snaps 62A, B can bedisposed on the bottom of the circuit board and the metal rivets 61A, Bcan be disposed on the top of the electrode 28.

The ECG module 40 is attached to a second electrode 24 with a geometrysimilar to that shown in FIG. 6B. Here, however, the electrode 24 is notelectrically connected to an internal ECG circuit, but rather is usedexclusively to hold the ECG module 40 in place. The electrode'sconductive regions connect through electrical leads within the module 40that are not connected to the ECG circuit, and then through a cable 68to the TBEV module 22. There, the module 22 receives and collectivelyprocesses signals from the first 28 and second 24 electrode to measure atime-dependent voltage as described above. This voltage is thenconverted into a time-dependent resistance to form the TBEV waveform,which is then processed to determine SV and ultimately CO. The cable 68also includes conductors for sending packets containing digitized ECGwaveforms and HR according to the CAN protocol. These packets passthrough CAN transceivers in the TBEV module 22, through the third cable29, and ultimately to the wrist-worn transceiver 30 for furtherprocessing and display.

In FIG. 6A the individual ECG electrodes 42A-C are disposed on a singlechest-worn patch 67 that attaches near the middle of the patient'sthorax. The patch 67 connects to the ECG module 40 through a singlecable 69 that includes individual conductors corresponding to eachelectrode 42A-C. These conductors port analog signals to the ECG module40, where they are analyzed as described above to determine ECGwaveforms and HR.

Within the body-worn monitor 19 are three three-axis accelerometers thatmeasure ACC waveforms corresponding to x, y, and z-axes. Theaccelerometers, which are not shown in the figure, are disposed withinthe ECG module 40, the TBEV module 22, and the wrist-worn transceiver30. During a measurement, ACC waveforms generated by the accelerometersare processed by microprocessors within each of the above-mentionedcomponents to determine a motion-related parameter. Measurements of SV(or any vital sign, for that matter) are rejected if the parameter, or asecondary parameter derived therefrom, is lower than the pre-determinedthreshold value. Algorithms for such calculations are described, forexample, in the following co-pending patent application, the contents ofwhich are incorporated herein by reference: VITAL SIGN MONITORING SYSTEMFEATURING 3 ACCELEROMETERS, U.S. Ser. No. 12/469,094 (filed May 20,2009).

Within the TBEV module is an analog circuit 100, shown in FIG. 7, thatperforms the TBEV measurement according to the invention. The figureshows just one embodiment of the circuit 100; similar electrical resultscan be achieved using a design and collection of electrical componentsthat differ from those shown in the figure.

The circuit 100 features a first electrode 105B that injects ahigh-frequency, low-amperage current (I_(in)) into the patient'sbrachium. This serves as the current source. Typically a current pump102 provides the modulated current, with the modulation frequencytypically being between 50-100 KHz, and the current magnitude beingbetween 0.1 and 10 mA. Preferably the current pump 102 supplies currentwith a magnitude of 4 mA that is modulated at 70 kHz through the firstelectrode 105B. A second electrode 104A serves as the current drain(I_(out)).

A pair of electrodes 104B, 105A measure the time-dependent voltageencountered by the propagating current. These electrodes are indicatedin the figure as V+ and V−. As described above, using Ohm's law (V=I×R),the measured voltage divided by the magnitude of the injected currentyields a time-dependent resistance to ac (i.e. impedance) that relatesto blood flow in the brachial artery. As shown by the waveform 128 inthe figure, the time-dependent resistance features a slowly varying dcoffset, characterized by Zo, that indicates the baseline impedanceencountered by the injected current; for TBEV this will depend, forexample, on the amount of fat, bone, muscle, and blood volume in thebrachium of a given patient. Zo, which typically has a value betweenabout 10 and 150Ω, is also influenced by low-frequency, time-dependentprocesses such as respiration. Such processes affect the inherentcapacitance near the brachial region that TBEV measures, and aremanifested in the waveform by low-frequency undulations, such as thoseshown in the waveform 128. A relatively small (typically 0.1-0.5Ω) accomponent, ΔZ(t), lies on top of Zo and is attributed to changes inresistance caused by the heartbeat-induced blood that propagates in thebrachial artery, as described in detail above. ΔZ(t) is processed with ahigh-pass filter to form a TBEV signal that features a collection ofindividual pulses 130 that are ultimately processed to ultimatelydetermine stroke volume and cardiac output.

Voltage signals measured by the first electrode 104B (V+) and the secondelectrode 105A (V−) feed into a differential amplifier 107 to form asingle, differential voltage signal which is modulated according to themodulation frequency (e.g. 70 kHz) of the current pump 102. From there,the signal flows to a demodulator 106, which also receives a carrierfrequency from the current pump 102 to selectively extract signalcomponents that only correspond to the TBEV measurement. The collectivefunction of the differential amplifier 107 and demodulator 106 can beaccomplished with many different circuits aimed at extracting weaksignals, like the TBEV signal, from noise. For example, these componentscan be combined to form a lock-in amplifier′ that selectively amplifiessignal components occurring at a well-defined carrier frequency. Or thesignal and carrier frequencies can be deconvoluted in much the same wayas that used in conventional AM radio using a circuit features one ormore diodes. The phase of the demodulated signal may also be adjustedwith a phase-adjusting component 108 during the amplification process.In one embodiment, the ADS 1298 family of chipsets marketed by TexasInstruments may be used for this application. This chipset featuresfully integrated analog front ends for both ECG and impedancepneumography. The latter measurement is performed with components fordigital differential amplification, demodulation, and phase adjustment,such as those used for the TBEV measurement, that are integrateddirectly into the chipset.

Once the TBEV signal is extracted, it flows to a series of analogfilters 110, 112, 114 within the circuit 100 that remove extraneousnoise from the Zo and ΔZ(t) signals. The first low-pass filter 1010 (30Hz) removes any high-frequency noise components (e.g. power linecomponents at 60 Hz) that may corrupt the signal. Part of this signalthat passes through this filter 110, which represents Zo, is porteddirectly to a channel in an analog-to-digital converter 120. Theremaining part of the signal feeds into a high-pass filter 112 (0.1 Hz)that passes high-frequency signal components responsible for the shapeof individual TBEV pulses 130. This signal then passes through a finallow-pass filter 114 (10 Hz) to further remove any high-frequency noise.Finally, the filtered signal passes through a programmable gainamplifier (PGA) 116, which, using a 1.65V reference, amplifies theresultant signal with a computer-controlled gain. The amplified signalrepresents ΔZ(t), and is ported to a separate channel of theanalog-to-digital converter 120, where it is digitized alongside of Zo.The analog-to-digital converter and PGA are integrated directly into theADS 1298 chipset described above. The chipset can simultaneouslydigitize waveforms such as Zo and ΔZ(t) with 24-bit resolution andsampling rates (e.g. 500 Hz) that are suitable for physiologicalwaveforms. Thus, in theory, this one chipset can perform the function ofthe differential amplifier 107, demodulator 108, PGA 116, andanalog-to-digital converter 120. Reliance of just a single chipset toperform these multiple functions ultimately reduces both size and powerconsumption of the TBEV circuit 100.

Digitized Zo and ΔZ(t) waveforms are received by a microprocessor 124through a conventional digital interface, such as a SPI or I2Cinterface. Algorithms for converting the waveforms into actualmeasurements of SV and CO are performed by the microprocessor 124. Themicroprocessor 124 also receives digital motion-related waveforms froman on-board accelerometer 122, and processes these to determineparameters such as the degree/magnitude of motion, frequency of motion,posture, and activity level.

FIGS. 8 and 9 show flow charts of algorithms 133A, B that function usingcompiled computer code that operates, e.g., on the microprocessor 124shown in FIG. 7. The compiled computer code is loaded in memoryassociated with the microprocessor, and is run each time a TBEVmeasurement is converted into a numerical value for CO and SV. Themicroprocessor typically runs an embedded real-time operating system.The compiled computer code is typically written in a language such as C,C++, or assembly language. Each step 135-151 in the different algorithms133A, B is typically carried out by a function or calculation includedin the compiled computer code.

Wrist-Worn Transceiver

The wrist-worn transceiver 272 used to perform the hybrid measurementmethod of SV according to the invention is shown in more detail in FIG.10. It features an embedded microprocessor (not shown in the figure) formaking these calculations, and a touch panel interface 273 that displaysCO/SV along with other properties described above. A flexible wriststrap 290 affixes the transceiver 272 to the patient's wrist like aconventional wristwatch. Connected to the transceiver 272 is an analogcable 292 that terminates with an optical sensor (not shown in thefigure) that wraps around the base of the patient's thumb to measure PPGwaveforms. During the measurement, the optical sensor generates a seriesof time-dependent PPG waveforms (measured with both red and infraredwavelengths) that a microprocessor in the transceiver processes alongwith a TBEV and ECG to measure cNIBP, SpO2, and provide waveforms to thehybrid measurement for SV/CO/CP.

As described above, the wrist-worn transceiver attaches to the patient'swrist using a flexible strap 290 which threads through two D-ringopenings in a plastic housing 206. The transceiver 272 features a touchpanel display 220 that renders a GUI 273 that is altered depending onthe viewer (typically the patient or a medical professional).Specifically, the transceiver 272 includes a small-scale infraredbarcode scanner 202 that, during use, can scan a barcode worn on a badgeof a medical professional. The barcode indicates to the transceiver'ssoftware that, for example, a nurse or doctor is viewing the userinterface. In response, the GUI 273 displays vital sign data and othermedical diagnostic information appropriate for medical professionals.Using this GUI 273, the nurse, doctor, or medical professional, forexample, can view the vital sign information, set alarm parameters, andenter information about the patient (e.g. their demographic information,medication, or medical condition). For example, for the SV/CO/CPmeasurement described above, the clinician can enter the patient'sgender, height, weight, and age. These parameters may be used in thecalculations described in Eq. 3, above, to estimate the Vc used in Eq. 3to calculate SV. Once entered, the clinician can press a button on theGUI 273 indicating that these operations are complete and that theappropriate data for the SV measurement has been entered. At this point,the display 220 renders an interface that is more appropriate to thepatient, such as one that simply displays the time of day and batterypower.

The transceiver 272 features three CAN connectors 204 a-c on the side ofits upper portion, each which supports the CAN protocol and wiringschematics, and relays digitized data to the transceiver's internal CPU.Digital signals that pass through the CAN connectors include a headerthat indicates the specific signal (e.g. TBEV, ECG, ACC, or numericalvalues calculated from these waveforms) and the sensor from which thesignal originated. In alternative embodiments some of these are sentfrom the chest-worn module through Bluetooth, which maintains the CANstructure of the packets. This allows the CPU to easily interpretsignals that arrive through the CAN connectors 204 a-c, such as thosedescribed above corresponding to TBEB and ECG waveforms, and means thatthese connectors are not associated with a specific cable. Any cableconnecting to the transceiver can be plugged into any connector 204 a-c.

As shown in FIG. 10, one embodiment of the invention features a firstconnector 204 a that receives a Bluetooth ‘dongle’ 208 that features anembedded antenna 213 and a connector 209 which snaps into and mates withany one of the CAN connectors 204 a-c. During operation the dongle 208is automatically paired with the Bluetooth transceiver in the chest-wornsensor, and then receives a digital data stream over Bluetooth with theantenna 213. An internal CAN transceiver formats the data stream intoCAN-compliant packets, and then passes these through the connector 209into the wrist-worn transceiver, where it is processed as describedabove.

The second CAN connector 204 b receives the cable 295 that connects toanother sensor, e.g. a pneumatic cuff-based system used to measure bloodpressure values used in the Composite Method. This connector 204 breceives a time-dependent pressure waveform delivered by the pneumaticsystem to the patient's arm, along with values for SYS, DIA, and MAPvalues determined during the Composite Method's indexing measurement.The cable 295 unplugs from the connector 204 b once the indexingmeasurement is complete, and is plugged back in after approximately fourhours for another indexing measurement.

The final CAN connector 204 c can be used for an ancillary device, e.g.a glucometer, infusion pump, body-worn insulin pump, NIRS system,ventilator, or et-CO2 measurement system. As described above, digitalinformation generated by these systems will include a header thatindicates their origin so that the CPU can process them accordingly.

The transceiver 272 includes a speaker 201 that allows a medicalprofessional to communicate with the patient using a voice over Internetprotocol (VOIP). For example, using the speaker 201 the medicalprofessional could query the patient from a central nursing station ormobile phone connected to a wireless, Internet-based network within thehospital. Or the medical professional could wear a separate transceiversimilar to the shown in the figure, and use this as a communicationdevice. In this application, the transceiver 272 worn by the patientfunctions much like a conventional cellular telephone or‘walkie-talkie’: it can be used for voice communications with themedical professional and can additionally relay information describingthe patient's vital signs and motion. The speaker can also enunciatepre-programmed messages to the patient, such as those used to calibratethe chest-worn accelerometers for a posture calculation, as describedabove.

Clinical Data from CO/SV Measurements

FIGS. 11A-E shows examples of ECG, ICG, and TBEV waveforms, along withtime-dependent derivatives of the both ICG (d(ICG)/dt) and TBEV(d(TBEV)/dt) waveforms. These data were simultaneously measured from ahuman subject over a 5-second period with a body-worn monitor similar tothat shown in FIG. 5. Each waveform features a heartbeat-induced ‘pulse’indicating a unique physiologic process. For example, the ECG waveformshown in FIG. 11A is measured with conventional ECG electrodes andcircuitry described above. It features a conventional QRS complexindicating rapid depolarization of the heart's right and leftventricles. Informally, the ECG waveform denotes the onset of thecardiac cycle. The ICG waveform (FIG. 11B) is measured from the thoraxusing a conventional ICG monitor and configuration of chest-wornelectrodes as described above, and its derivatized form (FIG. 11D)yields parameters used above in Eq. 5 to calculate SV. Specifically,from the derivatized waveform a computer program calculates parameterssuch as (dZ/dt)_(max) and SFT, which as described above corresponds tothe time period from opening of the aortic valve, heralding the onset ofejection, to closure of the aortic valve, signifying the end ofejection. The TBEV waveform (FIG. 11C) and its time-dependent derivative(FIG. 11E) are measured from the brachium using a configuration ofarm-worn electrodes similar to that shown in FIG. 5. The derivatizedTBEV waveform, like the derivatized ICG waveform, yields similarimpedance parameters such as (dZ/dt)_(max) and SFT, and features asignal-to-noise ratio similar to that shown for the ICG waveform,despite the fact that its genesis is the much smaller brachial artery.

FIGS. 12A, 12B indicate how LVET and SFT are extracted, respectively,from both the derivatized ICG and TBEV waveforms. As shown in FIG. 12A,the derivatized ICG waveform features consecutive pulses, eachcharacterized by three points: a ‘B’ point on the pulse's upswingindicating opening of the aortic valve; an X point on the pulse's nadirindicating closing of the aortic valve; and a ‘C’ point on its maximumvalue indicating the maximum slope of the ΔZ(t) pulse's upswing, whichis equivalent to (dZ/dt)_(max). LVET is typically calculated from thetime differential between the B and X points. However, due to the subtlenature of these fiducial markers, even low levels of noise in thewaveforms can make them difficult to determine. Ultimately such noiseadds errors to the calculated LVET and resulting SV.

Determining SFT from the derivatized TBEV waveform shown in FIG. 12B isrelatively easier. Here, there are no complicated B and X points. Theinitial upswing, or onset of the pulse, indicates the onset of flow,corresponding to opening of the aortic valve. And the second zerocrossing point after dZ/dt_(max), occurring later in time, indicateswhen acceleration of the erythrocytes is temporarily ceased. The secondzero crossing indicates the end of systolic forward flow, correspondingto closing of the valve. Computationally, using a computer algorithm,such a determination of SFT can be easily done by following theprogression of the pulse and recording the appropriate zero-pointcrossings.

FIGS. 13A-D indicate a technique for determining both an onset 90 anddichrotic notch 91, 95 in a pulse contained in a TBEV waveform. Asdescribed above, such fiducial markers can sometimes be obscured bybaseline noise (caused, e.g., by motion or low signal levels), makingthem difficult to determine. In the derivatized waveform in FIG. 13B, apair of data points 92 near the peak of the pulse can be selected andfit with a simple line 96, with the point at which the line 96intersects a zero value (shown in the figure by the dashed line 90)indicating the pulse's onset. Once this point is determined, thezero-point crossing indicating the dichrotic notch can be firstinitially estimated using an approximation for SFT, which is the timeseparating the pulse's onset and dichrotic notch. This is done using anequation known as ‘Weissler's Regression’, shown below in Eq. 6, thatestimates LVET from HR.LVET=−0.0017×HR+0.413  (6)Weissler's Regression allows LVET, equivalent to SFT 94, 95, to beestimated from HR determined from either the ECG waveform, oralternatively from PR determined from the PPG waveform. FIG. 13C depictsa derivative of the TBEV waveform showing a shaded region 93 indicatingwhere Weissler's regression is used to estimate LVET. LVET determinedfrom Weissler's relationship is also shown as a vertical line in FIGS.13A and 13D. FIG. 14 shows a correlation plot of a ‘fused’ SFTdetermined from HR and PR and compared to LVET from theDoppler/ultrasound for a 38-subject study. Here, Doppler/ultrasoundrepresents a gold standard for determining LVET. As is clear from thesedata, strong correlation (r=0.8) exists between these two methods,indicating that Eq. 6 is a reasonable way to determine SFT. This methodcan thus be used along with parameters extracted from TBEV signalsmeasured at the brachium to estimate SV.

To further support this point, FIGS. 15A-E show Doppler/ultrasoundimages measured from the brachial artery, and FIG. 15F showsconcurrently measured TBEV and ECG waveforms. These data indicate twoimportant aspects of the TBEV measurement. First, the Doppler/ultrasoundimages confirm that, during a typical cardiac cycle, volumetricexpansion in the brachial artery is minimal. The artery's diameterundergoes little to no measureable change during the cycle, meaning thatheartbeat-induced changes in blood conductivity, as measured by TBEV,are mostly due to acceleration of blood and the consequent parallelalignment of erythrocytes. Second, the images also indicate that thedichrotic notch in the TBEV waveform does indeed correspond to a pointin time when the acceleration of blood is temporarily zero, and thus SFTcan be accurately calculated from this fiducial marker.

More specifically, the figures show Doppler/ultrasound images indicatingforward blood velocity is zero prior to systole (FIG. 15A), therebyreducing conductivity and the corresponding amplitude of the TBEVwaveform. This point marks the onset of the TBEV pulse. Opening of theaortic valve induces systole (FIG. 15B) and increases acceleration ofblood and thus conductivity in the brachium, causing the TBEV waveformto rapidly increase in amplitude. Closure of the aortic valve, ascharacterized by SFT, marks the end of the systole (FIG. 15C) atemporary lull in acceleration, and consequently the appearance of thedichrotic notch. During diastole (FIG. 15D) flow is once again increaseddue to reflected waves, as well as blood remaining in the aorta, whichis injected into the brachium, and eventually decays away until thecycle is repeated with a new heartbeat (FIG. 15E). This relativelysimple physiology contrasts the complex, underlying physiologicalprocesses that take place in the thorax as mentioned above, which arethe basis for ICG-based determination of SV.

FIGS. 16A-D further illustrates how TBEV waveforms yield SFT in much thesame way as Doppler/ultrasound yields LVET, which as described aboverepresents a gold standard for this measurement. Here, FIG. 16A shows atime-dependent waveform extracted from a collection of two-dimensionalDoppler/ultrasound images, such as those shown in FIGS. 15A-E. Thewaveform indicates time-dependent blood velocity, and its derivatizedform is shown in FIG. 16C. Shown below these waveforms in FIGS. 16B and16D are simultaneously measured TBEV waveforms (FIG. 16B) and itsderivatized form (FIG. 16D). Dashed lines 97A, B, 98A, B in the figuresshow, respectively, the pulse onset (determined, e.g., as shown in FIG.13) and the dichrotic notch from the two sets of waveforms. As is clearfrom the figure, these points coincide exactly, indicating that LVETdetermined explicitly from Doppler/ultrasound waveforms is nearlyidentical to SFT determined from TBEV waveforms.

Another advantage of TBEV waveforms compared to those measured withconventional ICG is that they undergo little patient-to-patientvariation, thus making their computer-based analysis relatively easy.FIG. 17 demonstrates this point by showing derivatized waveforms from 7different subjects. Each waveform has roughly the same morphology, andin all cases the relevant fiducial makers (pulse onset, pulse maximum,zero-point crossing) are clear. This indicates that a simple computeralgorithm can be used to extract (dZ/dt)_(max) and SFT, which is thenused as described above to calculate SV.

The analysis described above was used in a formal clinical study to testaccuracy of determining CO using TBEV and Eq. 3 above, compared to COdetermined using Doppler/ultrasound. Correlation and Bland-Altman plotsare shown, respectively, in FIGS. 18A and 18B. The shaded gray area inthe plots indicates the inherent errors associated with conventionalDoppler/ultrasound measurements, which are about +/−20%. In total 23subjects (11M, 12W) with ages ranging from 21-80 were measured for thisstudy, and correlations for all but two of these subjects fell withinthe error of the Doppler/ultrasound measurements.

FIGS. 19A-B indicate that TBEV waveforms measured from the brachium maybe a better determinant of CO than ICG waveforms measured from thethorax. Here, both waveforms were measured while a subject wore a ‘MAST’suit, which is a pair of pressurized trousers used to force blood fromthe lower extremities toward the torso and the heart. A MAST suit thussimulates the reverse of hemorrhage, and thus causes CO/SV to increase.As shown in the figures by the dashed lines, LVET was determined fromICG, while SFT was determined using TBEV. Waveforms from thesetechniques were measured simultaneously from the thorax and brachiumduring period of high and low SV measurements. Increased SV is achievedwhen the MAST suit forces blood into the thorax. An increase in bothLVET and SFT indicate an increase in SV. In the ICG waveforms (FIG. 19A)only a small increase in LVET was detected. In contrast, in the TBEVwaveforms (FIG. 19B), a large increase in SFT was detected, indicatingmeasurements made in this region of the body may be more sensitive tosmall changes in SV and CO.

Measuring Respiration Rate with TBEV

TBEV, like techniques such as impedance pneumography, injects smallamounts of current into the patient's body, and measures resistance(i.e. impedance) encountered by the current to calculate a parameter ofinterest. During a TBEV measurement, heartbeat-induced blood flowresults in the pulsatile component of ΔZ(t). Additionally, changes incapacitance due to breathing may also affect the impedance as measuredby TBEV. FIGS. 20A-C illustrate this point. In FIG. 20A, for example, aTBEV waveform with no digital filtering shows both high-frequencycardiac components due to blood flow, as well as low-frequencyundulations due to respiration rate. Both features can be extracted andanalyzed using digital filtering. For example, as shown in FIG. 20B,processing the TBEV waveform shown in FIG. 20A with a first band-passfilter (0.5→15 Hz) removes the respiratory component, leaving only thecardiac component. Similarly, as shown in FIG. 20C, processing the TBEVwaveform shown in FIG. 20A with a second band-pass filter (0.001→1 Hz)removes the cardiac component, leaving on the undulations due torespiration. In this latter case, the peaks in the waveform can becounted with a conventional breath-picking algorithm to determinerespiration rate.

The algorithm for calculating respiration rate can be expanded toinclude processing of signal from the accelerometer within the TBEVmodule. For example, as shown in FIGS. 21A-D, these signals can becollectively processed to accurately determine respiration rate, even inthe presence of motion. A similar technique is described in thefollowing co-pending application, the contents of which are incorporatedherein by reference: BODY-WORN MONITOR FOR MEASURING RESPIRATION RATE,U.S. Ser. No. 12/762,874, filed Apr. 14, 2010. More specifically, FIGS.21A and 21B show time-domain TBEV and ACC waveforms measuredsimultaneously from a patient with the system similar to that describedabove. In the TBEV waveform, the slowly varying pulses occurringapproximately every 7 seconds correspond to individual breaths, whilethe sharp peaks in the waveform correspond to heartbeat-induced pulses.FIGS. 21C and 21D show, respectively, frequency-domain power spectra ofboth the TBEV and ACC waveforms. Clearly shown in the power spectra ofthe TBEV waveform is a dominant peak near 0.8 Hz corresponding to theheartbeat-induced pulses. A much weaker peak corresponding to thepatient's breathing rate is also evident near 0.15 Hz. As shown in thegray shaded region 99, the power spectra corresponding to the ACCwaveform features only one well-defined peak near 1.5 Hz that includesnearly the exact same frequency components as the corresponding peak inthe TBEV waveform. Further processing of these two spectra with a simplepeak-finding algorithm yields the patient's actual RR, which correspondsto about 8 breaths/minute.

Measuring TBEV Waveforms in the Absence of Motion

FIGS. 22A, B and 23A, B indicate how different degrees of motion from apatient's arm can influence both ECG and TBEV waveforms, therebyaffecting the accuracy of SV measurements. The ACC waveform is typicallymeasured along the vertical axis of the accelerometer embedded in theTBEV module. The magnitude of the axes for ECG and TBEV waveforms arethe same for all figures.

In FIG. 22B, for example, the ACC waveform is relatively flat and lacksany significant time-dependent features, indicating that the patient isnot moving and is relatively still. Consequently the TBEV waveform inFIG. 22A, which is strongly affected by motion, features well-definedvalues for the pulse onset, indicated by marker 73, and (dZ/dt)_(max),indicated by marker 74. Likewise the ECG waveform features a QRScomplex, indicated by marker 72, which is undistorted. The fidelity ofthese features indicate that both HR and SV values can typically beaccurately determined during periods of little or no motion, asindicated by the ACC waveform in FIG. 22B.

FIGS. 23A, B show the affects of a major amount of arm motion on boththe ECG and TBEV waveforms. Here, the period of motion is indicated inboth figures by the dashed box 80, which contrasts with the precedingperiod where motion is not present, as shown in the dashed box 81. TheACC waveform in FIG. 23B indicates that motion lasts for roughly onesecond, beginning and ending at times indicated, respectively, nearmarkers 78 and 79. The motion is complex and peaks in intensity atmarker 79. Even for major finger motion the ECG waveform and its QRScomplex, indicated by marker 75, are relatively undistorted. But theTBEV measured during the period of motion is strongly distorted to thepoint that its peak value, indicated by marker 77, is relatively flatand basically immeasurable. This makes it difficult to accuratelymeasure TBEV waveforms and the subsequent SV value calculated from thisparameter. The peak onset, indicated by marker 76, is also distorted,but to a lesser degree than the corresponding peak value.

Data shown in FIGS. 22A, B and 22A, B indicate that motion can bedetected and accounted for during TBEV measurements to minimize theoccurrence of false alarms and, additionally, make accurate readings inthe presence of motion. For example, during periods of motion SFT can becalculated using Weissler's Regression, and then used to Eq. 3 above toestimate SV. Or during such motion one of the various estimators shownin FIG. 1 could be used to estimate SV.

Processing ACC Waveforms to Determine Posture

A patient's posture may influence their values of SV/CO/CP, and thusknowing this parameter may improve the measurement described herein. Tomake this measurement, the body-worn monitor described above includesthree 3-axis accelerometers as well as the ECG and TBEV circuits. Inaddition to determining SV/CO/CP, these sensors can generatetime-dependent waveforms that when analyzed yield RR and the patient'smotion-related properties, e.g. degree of motion, posture, and activitylevel.

FIGS. 25A-B show, for example, that the body-worn monitor can generateACC waveforms that can be analyzed to accurately estimate the patient'sposture. Specifically, FIG. 25A shows that the 3-axis accelerometer inthe torso accurately measures ACC waveforms which correlate directly tothe patient's position (e.g. standing, lying on back, lying on chest,lying on side) as indicated in FIG. 25B. This property, in turn, can beused along with the patient's SV/CO/CP value and a series of ‘heuristicrules’ to generate an alarm/alert value. For example, an alarm need notbe sounded if the patient's SV/CO/CP value is low (e.g. below about 1.5l/min. for CO), but analysis of the ACC waveforms indicates that thepatient is standing or walking as shown in FIG. 25B. The assumption hereis that a patient in this posture/activity level is not in need ofmedical assistance. In contrast, a combination of a low SV/CO/CP valueand a patient that is either supine or, worse yet, recently fallenshould trigger an alarm.

FIG. 24 indicates how the body-worn monitor can determine motion-relatedparameters (e.g. degree of motion, posture, and activity level) from apatient 410 using time-dependent ACC waveforms continuously generatedfrom the three accelerometers 412, 413, 414 worn, respectively, on thepatient's chest, bicep, and wrist. Additionally, the height of thepatient's arm can affect the cNIBP measurement, as blood pressure canvary significantly due to hydrostatic forces induced by changes in armheight. Moreover, this phenomenon can be detected and exploited tocalibrate the cNIBP measurement, as described in detail in theabove-referenced patent applications, the contents of which have beenpreviously incorporated by reference. As described in these documents,arm height can be determined using DC signals from the accelerometers413, 414 disposed, respectively, on the patient's bicep and wrist.Posture, in contrast, can be exclusively determined by the accelerometer412 worn on the patient's chest. An algorithm operating on thewrist-worn transceiver extracts DC values from waveforms measured fromthis accelerometer and processes them with an algorithm described belowto determine posture.

Specifically, torso posture is determined for a patient 410 using anglesdetermined between the measured gravitational vector and the axes of atorso coordinate space 411. The axes of this space 411 are defined in athree-dimensional Euclidean space where {right arrow over (R)}_(CV) isthe vertical axis, {right arrow over (R)}_(CH) is the horizontal axis,and {right arrow over (R)}_(CN) is the normal axis. These axes must beidentified relative to a ‘chest accelerometer coordinate space’ beforethe patient's posture can be determined.

The first step in determining a patient's posture is to identifyalignment of {right arrow over (R)}_(CV) in the i chest accelerometercoordinate space. This can be determined in either of two approaches. Inthe first approach, {right arrow over (R)}_(CV) is assumed based on atypical alignment of the body-worn monitor relative to the patient.During a manufacturing process, these parameters are then preprogrammedinto firmware operating on the wrist-worn transceiver. In this procedureit is assumed that accelerometers within the body-worn monitor areapplied to each patient with essentially the same configuration. In thesecond approach, {right arrow over (R)}_(CV) is identified on apatient-specific basis. Here, an algorithm operating on the wrist-worntransceiver prompts the patient (using, e.g., video instructionoperating on the wrist-worn transceiver, or audio instructionstransmitted through a speaker) to assume a known position with respectto gravity (e.g., standing upright with arms pointed straight down). Thealgorithm then calculates {right arrow over (R)}_(CV) from DC valuescorresponding to the x, y, and z-axes of the chest accelerometer whilethe patient is in this position. This case, however, still requiresknowledge of which arm (left or right) the monitor is worn on, as thechest accelerometer coordinate space can be rotated by 180 degreesdepending on this orientation. A medical professional applying themonitor can enter this information using the GUI, described above. Thispotential for dual-arm attachment requires a set of two pre-determinedvertical and normal vectors that are interchangeable depending on themonitor's location. Instead of manually entering this information, thearm on which the monitor is worn can be easily determined followingattachment using measured values from the chest accelerometer values,with the assumption that {right arrow over (R)}_(CV) is not orthogonalto the gravity vector.

The second step in the procedure is to identify the alignment of {rightarrow over (R)}_(CN) in the chest accelerometer coordinate space. Themonitor determines this vector in the same way it determines {rightarrow over (R)}_(CV) using one of two approaches. In the first approachthe monitor assumes a typical alignment of the chest-worn accelerometeron the patient. In the second approach, the patient is prompted to thealignment procedure and asked to assume a known position with respect togravity. The monitor then calculates {right arrow over (R)}_(CN) fromthe DC values of the time-dependent ACC waveform.

The third step in the procedure is to identify the alignment of {rightarrow over (R)}_(CH) in the chest accelerometer coordinate space. Thisvector is typically determined from the vector cross product of {rightarrow over (R)}_(CV) and {right arrow over (R)}_(CN), or it can beassumed based on the typical alignment of the accelerometer on thepatient, as described above.

A patient's posture is determined using the coordinate system describedabove and in FIG. 24, along with a gravitational vector {right arrowover (R)}_(G) 416 that extends normal from the patient's chest. Theangle between {right arrow over (R)}_(CV) and {right arrow over (R)}_(G)is given by Eq. 7:

$\begin{matrix}{{\theta_{V\; G}\lbrack n\rbrack} = {\arccos\left( \frac{{{\overset{\rightharpoonup}{R}}_{G}\lbrack n\rbrack} \cdot {\overset{\rightharpoonup}{R}}_{C\; V}}{{{{\overset{\rightharpoonup}{R}}_{G}\lbrack n\rbrack}}{{\overset{\rightharpoonup}{R}}_{C\; V}}} \right)}} & (7)\end{matrix}$where the dot product of the two vectors is defined as:{right arrow over (R)} _(G) [n]·{right arrow over (R)} _(CV)=(y _(Cx)[n]×r _(CVx))+(y _(Cy) [n]×r _(CVy))+(y _(Cz) [n]×r _(CVz))  (8)The definitions of the norms of {right arrow over (R)}_(G) and R _(CV)are given by Eqs. 9 and 10:∥{right arrow over (R)} _(G) [n]∥=√{square root over ((y _(Cx) [n])²+(y_(Cy) [n])²(y _(Cz) [n])²)}  (9)∥{right arrow over (R)} _(Cv)∥=√{square root over ((r _(CVx))²(r_(CVy))²+(r _(CVz))²)}  (10)

As indicated in Eq. 12, the monitor compares the vertical angle θ_(VG)to a threshold angle to determine whether the patient is vertical (i.e.standing upright) or lying down:if θ_(VG)≦45° then Torso State=0,the patient is upright  (11)If the condition in Eq. 11 is met the patient is assumed to be upright,and their torso state, which is a numerical value equated to thepatient's posture, is equal to 0. The patient is assumed to be lyingdown if θ_(VG)>45 degrees. Their lying position is then determined fromangles separating the two remaining vectors, as defined below.

The angle θ_(NG) between {right arrow over (R)}_(CN) and {right arrowover (R)}_(G) determines if the patient is lying in the supine position(chest up), prone position (chest down), or on their side. Based oneither an assumed orientation or a patient-specific calibrationprocedure, as described above, the alignment of {right arrow over(R)}_(CN) is given by Eq. 11, where i, j, k represent the unit vectorsof the x, y, and z axes of the chest accelerometer coordinate spacerespectively:{right arrow over (R)} _(CN) =r _(CNx) îr _(CNy) ĵr _(CNz) k   (12)The angle between {right arrow over (R)}_(CN) and {right arrow over(R)}_(G) determined from DC values extracted from the chest ACC waveformis given by Eq. 13:

$\begin{matrix}{{\theta_{N\; G}\lbrack n\rbrack} = {\arccos\left( \frac{{{\overset{\rightharpoonup}{R}}_{G}\lbrack n\rbrack} \cdot {\overset{\rightharpoonup}{R}}_{C\; N}}{{{{\overset{\rightharpoonup}{R}}_{G}\lbrack n\rbrack}}{{\overset{\rightharpoonup}{R}}_{C\; N}}} \right)}} & (13)\end{matrix}$The body-worn monitor determines the normal angle θ_(NG) and thencompares it to a set of predetermined threshold angles to determinewhich position in which the patient is lying, as shown in Eq. 14:if θ_(NG)≦35° then Torso State=1,the patient is supineif θ_(NG)≧135° then Torso State=2,the patient is prone  (14)If the conditions in Eq. 14 are not met then the patient is assumed tobe lying on their side. Whether they are lying on their right or leftside is determined from the angle calculated between the horizontaltorso vector and measured gravitational vectors, as described above.

The alignment of {right arrow over (R)}_(CH) is determined using eitheran assumed orientation, or from the vector cross-product of {right arrowover (R)}_(CV) and {right arrow over (R)}_(CN) as given by Eq. 15, wherei, j, k represent the unit vectors of the x, y, and z axes of theaccelerometer coordinate space respectively. Note that the orientationof the calculated vector is dependent on the order of the vectors in theoperation. The order below defines the horizontal axis as positivetowards the right side of the patient's body.{right arrow over (R)} _(CH) =r _(CVx) î+r _(CVy) ĵ+r _(CVz) {circumflexover (k)}={right arrow over (R)} _(CV) ×{right arrow over (R)}_(CN)  (15)The angle θ_(HG) between {right arrow over (R)}_(CH) and {right arrowover (R)}_(G) is determined using Eq. 16:

$\begin{matrix}{{\theta_{H\; G}\lbrack n\rbrack} = {\arccos\left( \frac{{{\overset{\rightharpoonup}{R}}_{G}\lbrack n\rbrack} \cdot {\overset{\rightharpoonup}{R}}_{C\; H}}{{{{\overset{\rightharpoonup}{R}}_{G}\lbrack n\rbrack}}{{\overset{\rightharpoonup}{R}}_{C\; H}}} \right)}} & (16)\end{matrix}$The monitor compares this angle to a set of predetermined thresholdangles to determine if the patient is lying on their right or left side,as given by Eq. 17:if θ_(HG)≧90° then Torso State=3,the patient is on their right sideif θ_(NG)<90° then Torso State=4,the patient is on their left side  (17)Table 1 describes each of the above-described postures, along with acorresponding numerical torso state used to render, e.g., a particularicon on a remote computer:

TABLE 2 postures and their corresponding torso states Posture TorsoState standing upright 0 supine: lying on back 1 prone: lying on chest 2lying on right side 3 lying on left side 4 undetermined posture 5Data shown in FIGS. 25A, B were calculated using the above-mentionedapproach. As the patient moves, the DC values of the ACC waveformsmeasured by the chest accelerometer vary accordingly, as shown in FIG.25A. The body-worn monitor processes these values as described above tocontinually determine {right arrow over (R)}_(G) and the variousquantized torso states for the patient, as shown in FIG. 25B. The torsostates yield the patient's posture as defined in Table 2. For this studythe patient rapidly alternated between standing, lying on their back,chest, right side, and left side within a time period of about 160seconds. Different alarm/alert conditions (e.g. threshold values) forvital signs can be assigned to each of these postures, or the specificposture itself may result in an alarm/alert. Additionally, thetime-dependent properties of the graph can be analyzed (e.g. by countingchanges in torso states) to determine, for example, how often thepatient moves in their hospital bed. This number can then be equated tovarious metrics, such as a ‘bed sore index’ indicating a patient that isso stationary in their bed that lesions may result.

Alternate Embodiments

Other embodiments are within the scope of the invention. For example,the TBEV harness and its associated electrode can take on a variety ofconfigurations. One of these is shown in FIG. 27. Here, the TBEV harness170 features a TBEV module 156 disposed directly on top of a single TBEVelectrode 158. The electrode 158 features four conductive regions (notshown in the figure): 1) a current source; 2) a current sink; and 3), 4)a pair of electrodes for measuring a voltage in the CB region. Asdescribed above, conductive regions for sourcing and draining thecurrent are on an outer portion of the electrode 158, while those formeasuring voltage are on an inner portion of the electrode. Eachconductive region connects to analog circuitry within the TBEV module156 with a single connector (not shown in the figure). The TBEV module156 also includes a CAN transceiver (not shown in the figure) that sendsdigitized waveforms and CO/SV values through a first cable 154 to aconnector 152 which plugs into the back panel of the wrist worntransceiver, such as that shown in FIG. 10. A second cable 160 connectsto an ECG module 162, which in turn connects through a short third cable164 to a collection of ECG leads 166. During a measurement, the ECGmodule 163 sends digitized versions of ECG waveforms, HR, and otherinformation through the second cable 160 and to the TBEV module 156.Data are sent according to the CAN protocol. From there, data arerelayed with the module's internal CAN transceivers through the secondcable 154 and to the connector 152, which then passes the data onto thewrist transceiver.

FIG. 28 shows an alternate embodiment of the invention where TBEV 449and ECG 420 modules are physically separated and connected through awireless interface. Here, the ECG module 420 includes the ECG circuit,and attaches through cables 430 a-c to ECG electrodes 424 a-c. A secondarm-worn module 449 includes four electrodes (two for injecting current;two for measuring voltage) dispersed on its upper and lower portionsthat connect to a central TBEV circuit to perform a measurement at thebrachium as described above. Both the chest-worn module 420 and arm-wornmodule 449 include a unique Bluetooth transmitter that sends,respectively, ECG and TBEV waveforms to a paired Bluetooth transmitter428 in the wrist-worn transceiver 426.

FIG. 29 shows the arm-worn module 449 in more detail. As describedabove, it includes four electrodes 448 a-d that snap on to the backsurface of a flexible substrate 451 that holds an TBEV circuit 447,located in the module's center in place. The electrodes 448 a-d providethe current-injecting and voltage-measuring functions of the TBEVmeasurement as described above, and connect to the TBEV circuit 447through a series of metal traces 453 a-d embedded within the flexiblesubstrate. The electrodes 448 a-d also adhere to the patient's skin tohold the module 449 on the brachium. Once a TBEV waveform is measured, aBluetooth transmitter 446 located at the bottom of the module sends itto the wrist-worn transceiver for processing, as described above.

FIGS. 30 and 31 show schematic drawings of an alternate embodiment ofthe invention, and indicate how data relating to SV/CP/CP can bewirelessly transmitted from the chest-worn sensor 500 and wrist-worntransceiver 506 to an external router (517 a in FIG. 30, 517 b in FIG.31), and from there to an external network (e.g. the Internet). Multiplestrategies can be used for this data-transfer process, two of which areshown in the figures. In FIG. 30, for example, the module 500 attachesthrough cables 510 a-c to electrode patches 502 a, 502 b, and 504. ICGand ECG waveforms are measured from analog signals collected byelectrode patches 502 a, 502 b, 504, and then wirelessly transmittedusing Bluetooth module 501 from the chest-worn sensor 500 to thewrist-worn transceiver 506, where they are received using Bluetoothmodule 508 and then analyzed as described above to determine SV/CO/CP,along with all other vital signs. The wrist-worn transceiver 506additionally connects through a short cable 509 that carries only analogsignals measured by a thumb-worn optical sensor 507. These processeddata are then sent from the transceiver 506 to the external router 517 ausing Bluetooth, 802.11, or any other wireless protocol. Once the router506 receives the data, it transmits it out using a wireless protocol(e.g. CDMA, GSM, iDEN) or wired protocol (e.g. Ethernet) to the externalnetwork. From there, for example, the data can be transferred to ahospital medical records systems, website, or sent through a web serviceto another application.

FIG. 31 shows an alternate approach where the external router 517 bperforms a higher degree of the computing load. In this case, thechest-worn sensor 500 processes analog signals measured by the electrodepatches 502 a, 502 b, 504 to determine ECG and ICG waveforms, and thenwirelessly transmits these in a digital form to the router 517 b. Ataround the same time, the wrist-worn transceiver measures SpO2 and PPGwaveforms and wirelessly transmits these to the router 517 b. There, anembedded processor analyzes ECG waveforms to determine HR; ECG and PPGwaveforms to determine PAT and cNIBP; and ECG, ICG, PPG waveforms andPAT to determine SV/CO/CP. These data are then transmitted as describedabove to the external network, and from there to another system.

In addition to those methods described above, the body-worn monitor canuse a number of additional methods to calculate blood pressure and otherproperties from the optical and electrical waveforms. These aredescribed in the following co-pending patent applications, the contentsof which are incorporated herein by reference: 1) CUFFLESSBLOOD-PRESSURE MONITOR AND ACCOMPANYING WIRELESS, INTERNET-BASED SYSTEM(U.S. Ser. No. 10/709,015; filed Apr. 7, 2004); 2) CUFFLESS SYSTEM FORMEASURING BLOOD PRESSURE (U.S. Ser. No. 10/709,014; filed Apr. 7, 2004);3) CUFFLESS BLOOD PRESSURE MONITOR AND ACCOMPANYING WEB SERVICESINTERFACE (U.S. Ser. No. 10/810,237; filed Mar. 26, 2004); 4) CUFFLESSBLOOD PRESSURE MONITOR AND ACCOMPANYING WIRELESS MOBILE DEVICE (U.S.Ser. No. 10/967,511; filed Oct. 18, 2004); 5) BLOOD PRESSURE MONITORINGDEVICE FEATURING A CALIBRATION-BASED ANALYSIS (U.S. Ser. No. 10/967,610;filed Oct. 18, 2004); 6) PERSONAL COMPUTER-BASED VITAL SIGN MONITOR(U.S. Ser. No. 10/906,342; filed Feb. 15, 2005); 7) PATCH SENSOR FORMEASURING BLOOD PRESSURE WITHOUT A CUFF (U.S. Ser. No. 10/906,315; filedFeb. 14, 2005); 8) PATCH SENSOR FOR MEASURING VITAL SIGNS (U.S. Ser. No.11/160,957; filed Jul. 18, 2005); 9) WIRELESS, INTERNET-BASED SYSTEM FORMEASURING VITAL SIGNS FROM A PLURALITY OF PATIENTS IN A HOSPITAL ORMEDICAL CLINIC (U.S. Ser. No. 11/162,719; filed Sep. 9, 2005); 10)HAND-HELD MONITOR FOR MEASURING VITAL SIGNS (U.S. Ser. No. 11/162,742;filed Sep. 21, 2005); 11) CHEST STRAP FOR MEASURING VITAL SIGNS (U.S.Ser. No. 11/306,243; filed Dec. 20, 2005); 12) SYSTEM FOR MEASURINGVITAL SIGNS USING AN OPTICAL MODULE FEATURING A GREEN LIGHT SOURCE (U.S.Ser. No. 11/307,375; filed Feb. 3, 2006); 13) BILATERAL DEVICE, SYSTEMAND METHOD FOR MONITORING VITAL SIGNS (U.S. Ser. No. 11/420,281; filedMay 25, 2006); 15) SYSTEM FOR MEASURING VITAL SIGNS USING BILATERALPULSE TRANSIT TIME (U.S. Ser. No. 11/420,652; filed May 26, 2006); 16)BLOOD PRESSURE MONITOR (U.S. Ser. No. 11/530,076; filed Sep. 8, 2006);17) TWO-PART PATCH SENSOR FOR MONITORING VITAL SIGNS (U.S. Ser. No.11/558,538; filed Nov. 10, 2006); and, 18) MONITOR FOR MEASURING VITALSIGNS AND RENDERING VIDEO IMAGES (U.S. Ser. No. 11/682,177; filed Mar.5, 2007).

Other embodiments are also within the scope of the invention. Forexample, other measurement techniques, such as conventional oscillometrymeasured during deflation, can be used to determine SYS for theabove-described algorithms. Additionally, processing units and probesfor measuring SpO2 similar to those described above can be modified andworn on other portions of the patient's body. For example, opticalsensors with finger-ring configurations can be worn on fingers otherthan the thumb. Or they can be modified to attach to other conventionalsites for measuring SpO2, such as the ear, forehead, and bridge of thenose. In these embodiments the processing unit can be worn in placesother than the wrist, such as around the neck (and supported, e.g., by alanyard) or on the patient's waist (supported, e.g., by a clip thatattaches to the patient's belt). In still other embodiments the probeand processing unit are integrated into a single unit.

In other embodiments, a set of body-worn monitors can continuouslymonitor a group of patients, wherein each patient in the group wears abody-worn monitor similar to those described herein. Additionally, eachbody-worn monitor can be augmented with a location sensor. The locationsensor includes a wireless component and a location-processing componentthat receives a signal from the wireless component and processes it todetermine a physical location of the patient. A processing component(similar to that described above) determines from the time-dependentwaveforms at least one vital sign, one motion parameter, and an alarmparameter calculated from the combination of this information. Awireless transceiver transmits the vital sign, motion parameter,location of the patient, and alarm parameter through a wireless system.A remote computer system featuring a display and an interface to thewireless system receives the information and displays it on a userinterface for each patient in the group.

In embodiments, the interface rendered on the display at the centralnursing station features a field that displays a map corresponding to anarea with multiple sections. Each section corresponds to the location ofthe patient and includes, e.g., the patient's vital signs, motionparameter, and alarm parameter. For example, the field can display a mapcorresponding to an area of a hospital (e.g. a hospital bay or emergencyroom), with each section corresponding to a specific bed, chair, orgeneral location in the area. Typically the display renders graphicalicons corresponding to the motion and alarm parameters for each patientin the group. In other embodiments, the body-worn monitor includes agraphical display that renders these parameters directly on the patient.

Typically the location sensor and the wireless transceiver operate on acommon wireless system, e.g. a wireless system based on 802.11 (i.e.‘WiFi’), 802.15.4 (i.e. ‘Bluetooth’), or cellular (e.g. CDMA, GSM)protocols. In this case, a location is determined by processing thewireless signal with one or more algorithms known in the art. Theseinclude, for example, triangulating signals received from at least threedifferent base stations, or simply estimating a location based on signalstrength and proximity to a particular base station. In still otherembodiments the location sensor includes a conventional globalpositioning system (GPS) that processes signals from orbiting satellitesto determine patient's position.

The body-worn monitor can include a first voice interface, and theremote computer can include a second voice interface that integrateswith the first voice interface. The location sensor, wirelesstransceiver, and first and second voice interfaces can all operate on acommon wireless system, such as one of the above-described systems basedon 802.11 or cellular protocols. The remote computer, for example, canbe a monitor that is essentially identical to the monitor worn by thepatient, and can be carried or worn by a medical professional. In thiscase the monitor associated with the medical professional features a GUIwherein the user can select to display information (e.g. vital signs,location, and alarms) corresponding to a particular patient. Thismonitor can also include a voice interface so the medical professionalcan communicate directly with the patient.

Still other embodiments are within the scope of the following claims.

What is claimed is:
 1. A method for determining stroke volume from apatient using a body-worn device, the method comprising: measuring anelectrocardiogram (ECG waveform from a first portion on the patient'sbody with an ECG module comprising: i) at least two electrodes; and ii)an ECG circuit configured to process signals from the at least two ECGelectrodes to generate the ECG waveform; measuring a trans-brachialelectro-velocimetry (TBEV) waveform from a second portion on thepatient's body with an impedance module comprising: i) at least twoimpedance electrodes; and ii) an impedance circuit configured to processsignals from the at least two impedance electrodes to generate the TBEVwaveform; measuring a photoplethysmograph (PPG) waveform from a thirdportion on the patient's body with an optical module comprising: i) anoptical probe; and ii) an optical circuit configured to process signalsfrom the optical probe to generate the PPG waveform; processing the TBEVwaveform to determine a (dZ/dt)max value; determining a systolic flowtime (SFT value by processing at least one of: i) the PPG waveform; ii)the TBEV waveform; and iii) the ECG waveform; and determining a strokevolume value by processing the SFT value and the (dZ/dt)max value.
 2. Amethod for determining stroke volume from a patient using a body-worndevice, the method comprising: measuring an ECG waveform from apatient's chest with an ECG module comprising: i) at least twoelectrodes; and ii) an ECG circuit, the ECG circuit configured toprocess the ECG signal from the ECG electrodes; measuring a TBEVwaveform from a patient's brachium with an impedance module comprising:i) at least two impedance electrodes; and ii) an impedance circuit, theimpedance circuit configured to process the at least one impedancesignal from the at least two impedance electrodes to generate the TBEVwaveform; measuring an optical signal from a patient's hand with anoptical module comprising: i) an optical probe; and ii) an opticalcircuit configured to process the optical signal from the optical probeto generator the PPG waveform; processing the TBEV waveform to determinea (dZ/dt)max value; determining a SFT value by processing at least oneof: i) the PPG waveform; ii) the TBEV waveform; and iii) the ECGwaveform; and determining a stroke volume value by processing the SFTvalue and the (dZ/dt)max value.
 3. The method of claim 1, wherein the atleast two ECG electrodes are proximal to the patient's chest.
 4. Themethod of claim 1, wherein the at least two impedance electrodes areproximal to the patient's brachium.
 5. The method of claim 1, whereinthe optical probe is proximal to the patient's hand.
 6. The method ofclaim 1, wherein the body-worn device further comprises an accelerometermodule configured to measure a time-dependent motion waveform from apatient.
 7. The method of claim 6, wherein the body-worn device furthercomprises a processor module configured to compare the time-dependentmotion waveform or at least one value derived from the time-dependentmotion waveform to determine a motion value.
 8. The method of claim 7,wherein the processor module is further configured to compare the motionvalue to a pre-determined threshold value.
 9. The method of claim 8,wherein the processor module is further configured to reject a strokevolume value if the motion value exceeds the pre-determined thresholdvalue.
 10. The method of claim 9, wherein the processor module isfurther configured to process the TBEV waveform only if the motion valueis less than the pre-determined threshold value.
 11. The method of claim1, wherein the (dZ/dt)max value is a derivative value of the TBEVwaveform.
 12. The method of claim 11, wherein the quotient of a(dZ/dt)max value and a corresponding value for Z₀ are further processedby taking its square root and further processing this value and the SFTvalue to determine the stroke volume value.
 13. The method of claim 1,wherein the PPG waveform is further processed to determine the dichroticnotch, and analyzing a time-dependent value associated with the dichoticnotch to determine the SFT value.
 14. The method of claim 1, wherein theECG waveform is further processed to determine a heart rate (HR) value.15. The method of claim 14, wherein the HR is processed to estimate avalue of SFT.
 16. The method of claim 1, wherein the impedance module isconnected to the body-worn device through wired interface.
 17. Themethod of claim 16, wherein the impedance module is further configuredto transmit a digital representation of the TBEV waveform through acable on the body-worn device.
 18. The method of claim 1, wherein theECG module is connected to the body-worn device through a wiredinterface.
 19. The method of claim 18, wherein the ECG module is furtherconfigured to transmit a digital representation of the ECG waveformthrough a cable on the body-worn device.
 20. The method of claim 1,wherein the optical module is further configured to connect to thebody-worn device through a wired interface.